Emission of atmospheric pollutants during forest fire in boreal region of China

Release of biogenic volatile organic compounds and physiological responses of two sub-tropical tree species to smoke derived from forest fire

Forests emit a large amount of biogenic volatile organic compounds (BVOCs) in response to biotic and abiotic stress. Despite frequent occurrence of large forest fires in recent years, the impact of smoke stress derived from these forest fires on the emission of BVOCs is largely unexplored. Thus, the aims of the study were to quantify the amount and composition of BVOCs released by two sub-tropical tree species, Cunninghamia lanceolata and Schima superba, in response to exposure to smoke. Physiological responses and their relationship with BVOCs were also investigated. The results showed that smoke treatments significantly (p < 0.001) promoted short-term release of BVOCs by C. lanceolata leaves than S. superba; and alkanes, olefins and benzene homologs were identified as major classes of BVOCs. Both C. lanceolata and S. superba seedlings showed significant (p < 0.005) physiological responses after being smoke-stressed where photosynthetic rate remained unaffected, chlorophyll content greatly reduced and Activities of anti-oxidant enzymes and the malondialdehyde content generally increased with the increase in smoke concentration. Activities of anti-oxidant enzymes showed mainly positive correlations with the major BVOCs. In conclusion, the release of BVOCs following smoke stress is species-specific and there exists a link between activities of antioxidant enzymes and BVOCs released. The findings provide insight about management of forest fires in order to control excessive emission of smoke that would trigger increased release of BVOCs.

Effect of agricultural soil wind erosion on urban PM2.5 concentrations simulated by WRF-Chem and WEPS: A case study in Kaifeng, China

Dust emission induced by agricultural soil wind erosion is one of the main sources of atmospheric particulate matter (PM) in dryland areas. However, most current air quality models do not consider this emission source, resulting in large uncertainties in PM simulations. Here we estimated the agricultural PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm) emission around Kaifeng, a prefecture-level city in central China, using the Wind Erosion Prediction System (WEPS), with the MEIC (Multi-resolution Emission Inventory for China) as an anthropogenic emission source. We then plugged these estimates into the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an air pollution episode in Kaifeng, China. Results showed that the addition of agricultural soil PM_2.5 emissions significantly improved the ability of WRF-Chem to accurately simulate PM_2.5 concentrations. The PM_2.5 concentration mean bias and correlation coefficient of not considered and considered agricultural dust emission were -72.35 μg m^-‍3 and 3.31 μg m^-‍3 and 0.3 and 0.58, respectively. The PM_2.5 emitted by the agricultural soil wind erosion contributed around 37.79% of the PM_2.5 in the Kaifeng municipal district during this pollution episode. This study confirmed that the dust emission caused by agricultural soil wind erosion can significantly impact urban PM_2.5 concentrations which surrounded by large areas of farmland, and also indicated that coupling dust emissions from farmland with anthropogenic air pollutant emissions can improve the accuracy of air quality models.

Trace gas emissions from laboratory combustion of leaves typically consumed in forest fires in Southwest China

Forest fires are becoming increasingly severe and frequent due to global climate change. Trace gases emitted from forest fires significantly affect atmospheric chemistry and climate change on a regional and global scale. Forest fires occur frequently in Southwest China, but systematic studies on trace gas emissions from forest fires in Southwest China are rare. Leaves of seven typical vegetation fuels based on their prominence in forest fires consumption in Southwest China were burned in a self-designed combustion device and the emission factors of eighteen trace gases (greenhouse gases, non-methane organic gases, nitrogenous gases, hydrogen chloride, and sulfur dioxide) at specific combustion stages (flaming and smoldering) were determined by using Fourier transform infrared spectroscopy, respectively. The emission factors data presented were compared with previous studies and can aid in the construction of an emission inventory. Pine needle combustion released a greater amount of methane in the smoldering stage than other broadleaf combustion. Peak values of emission factors for methane and non-methane organic gas are emitted by the smoldering of vegetation (Pinus kesiya and Pinus yunnanensis), which is endemic to forest fires in Southwest China. The emission factor for oxygenated volatile organic compounds (OVOCs) in the smoldering stage is greater than the flaming stage. This work established the relationship between modified combustion efficiency (MCE) with emission factors of hydrocarbons (except acetylene) and OVOCs. The results show that exponential fitting is more suitable than linear fitting for the seven leaf fuels (four broadleaf and three coniferous). However, the emission factors from the combustion of three coniferous fuels relative to all fuels are linear with MCE. Findings demonstrated that different combustion stages and fuel types have significant impacts on the emission factors, which also highlighted the importance of studying regional emissions.

Capture of fire smoke particles by leaves of Cunninghamia lanceolata and Schima superba, and importance of leaf characteristics

Emission of particulate matter (PM) during forest fires is a major source of air pollution and hence purification of atmospheric pollution has gained increasing importance. Trees can absorb polluting gases and fine particles by their leaves from the atmosphere and act as a sustainable air purification filter. However, the capture efficiency varies among tree species; thus exploring the ability of forest trees to capture smoke PM released during forest fires provides a basis for assessing net emissions from forest fires and the impact of smoke on forest ecosystems. In this study, the main afforestation tree species, Cunninghamia lanceolata (Lamb.) Hook, and a fire-resistant tree species, Schima superba Gardn.et Champ, in southern China were exposed to different smoke concentrations by simulating forest fire. The amount of PM per unit leaf area, absorption of nutrient element, leaf surface characteristics and antioxidant enzyme activities were determined. The main findings were: (1) The total quantity of PM captured by unit leaf area (μg·cm^-2) of C. lanceolata was 28.25 ± 1.12, 30.52 ± 3.43 and 33.14 ± 3.00 in low, intermediate and high smoke concentrations, respectively. The corresponding values for S. superba was 5.96 ± 0.56, 10.09 ± 1.13 and 12.27 ± 0.39, respectively. (2) Both species had weak absorption capacity for inorganic ions in the PM. (3) The purification of smoke PM by leaves was mainly related to leaf surface roughness, where it was higher for C. lanceolata than S. superba leaves. (4) Smoke treatment positively affected the contents of chlorophyll and soluble protein as well as increased antioxidant enzyme activities. In conclusion, the findings highlight the importance of leaf structural characteristics in capturing smoke particles and C. lanceolata is better suited for purification of atmospheric smoke particles following forest fire than S. superba.

Dynamics of pollutant emissions from wildfires in Mainland China

We analyzed the dynamics of pollutant emissions from wildfires in mainland China from 2001 to 2019 using MODIS fire products combined with the measurements of emission factors of different vegetation types. The biomass distribution in Mainland China has heterogeneous temporal and spatial pattern, with inter-year variations and a decreasing trend from east to west. Overall, from 2001 to 2019, biomass combustion in Mainland China reached 479.59 Tg (25.24 Tg·a-1), in which northeast, north, east, south, central, northwest, and southwest regions accounted for 20.95%, 31.14%, 8.89%, 9.06%, 3.98%, 0.33% and 25.64% of total biomass combustion, respectively. The emissions of CO, CO₂, CxHy, NOx, PM_2.5, TC, OC and EC were 47.30, 288.05, 12.90, 0.40, 1.43, 0.83, 0.70, and 0.12 Tg (1 Tg = 10¹²g), respectively. PM_2.5, TC and OC emissions increased in the southwest, while all pollutant emissions declined significantly in the southern region. For particulate matter from wildfires, both the ratio of its emissions to total dust and the ratio of its concentration to atmospheric PM_2.5 showed an increasing trend, implying that the relative environmental impacts of particulate emissions from wildfires may be rising. In addition, our results show that the current Chinese wildfire management has successfully reduced on average more than 80% of pollutant emissions from wildfire from 2001 to 2019 compared to the natural wildfire regime (no strict wildfire management). This research on the temporal-spatial changes of pollutant emissions from wildfires in Mainland China provides support for further exploration of wildfire impacts on regional environments, and indicates the effectiveness of Chinese current wildfire policy on the pollutant emission mitigation.

Experimental Characterization of Particulate and Gaseous Emissions from Biomass Burning of Six Mediterranean Species and Litter

Wildfires across the Mediterranean ecosystems are associated with safety concerns due to their emissions. The type of biomass determines the composition of particulate matter (PM) and gaseous compounds emitted during the fire event. This study investigated simulated fire events and analysed biomass samples of six Mediterranean species and litter in a combustion chamber. The main aims are the characterization of PM realized through scanning electron microscopy (SEM/EDX), the quantification of gaseous emissions through gas chromatography (GC-MS) and, consequently, identification of the species that are potentially more dangerous. For PM, three size fractions were considered (PM10, 2.5 and 1), and their chemical composition was used for particle source-apportionment. For gaseous components, the CO, CO2, benzene, toluene and xylene (BTXs) emitted were quantified. All samples were described and compared based on their peculiar particulate and gaseous emissions. The primary results show that (a) Acacia saligna was noticeable for the highest number of particles emitted and remarkable values of KCl; (b) tree species were related to the fine windblown particles as canopies intercept PM10 and reemit it during burning; (c) shrub species were related to the particles resuspended from soil; and (d) benzene and toluene were the dominant aromatic compounds emitted. Finally, the most dangerous species identified during burning were Acacia saligna, for the highest number of particles emitted, and Pistacia lentiscus for its high density of particles, the presence of anthropogenic markers, and the highest emissions of all gaseous compounds.

Spatial distribution of particulate matter 2.5 released from surface fuel combustion of Pinus koraiensis - A laboratory simulation study

High concentration particulate matter 2.5 released from forest fires, in addition to direct burns and asphyxia, PM_2.5 is one of the main pollutants which threaten the safety of forest fire fighter. Therefore, to assess spatial distribution of PM_2.5, a simulation study was conducted. Fuel beds with different moisture contents and loads were constructed. 144 times burning experiments were carried out under different wind speeds by using wind tunnel device. PM_2.5 particles at different spatial points were collected and calculated. The results show that, in the two of three variables interaction between wind speed, fuel load, and, except fuel moisture content, wind speed and fuel load are positively correlated with the PM_2.5 concentrations. From PM_2.5 concentration which collected at each point in the horizontal and vertical directions, the overall trend is that PM_2.5 concentration increases along the horizontal downwind direction (C and D higer than A and B) and the vertical upward direction (A and C higer than B and D) Based on BP neural network, the spatial distribution model of PM_2.5 concentration with single hidden layer was established. The prediction accuracy of modeling samples and validation samples is balanced when hidden layer node is 5. This study will help to make reference for PM_2.5 occupational exposure standards, forest fire smoke management and forest fire management in China.

Mathematical Simulation of Forest Fuel Pyrolysis in One-Dimensional Statement Taking into Account Soot Formation

Pyrolysis (thermal decomposition) is considered as the most important stage of a forest fire before direct forest fuel ignition. This process is accompanied by soot particle formation. Such particles have a negative impact on public health in the vicinity of forest fires. The purpose of this article was to investigate the heat and mass transfer process occurring in a typical forest fuel element (birch leaf). The pyrolysis and soot formation processes were taken into account, and various forest fire scenarios were considered. Computational experiments were carried out using the high-level programming language Delphi. Heat and mass transfer processes were described by nonlinear non-stationary differential heat conduction equations with corresponding initial and boundary conditions. The differential equations were solved by the finite difference method. Nonlinearity was resolved using a simple iteration. The main results of the research were (1) physical and mathematical models proposed for modeling forest fuel pyrolysis, taking into account soot formation and conditions corresponding to various forest fires; (2) a computer program coded in the high-level programming language Delphi; (3) the obtained temperature distributions over leaf thickness; (4) volume fractions obtained for various components dependent on time and space coordinates. The qualitative analysis of the dependencies showed that the temperature distributions in the birch leaf structure are similar for all forest fire types and differ only in absolute value. The intensity of the soot formation process directly depends on the forest fire type. The presented results should be useful in predicting and assessing forest fire danger, including near the facilities of the Russian Railways.

Effect of forest floor fuel moisture content on chemical components of PM2.5 emitted during combustion

The moisture content of forest floor fuels changes continuously with the influence of environmental factors; thus it has an important impact on the concentration and chemical composition of particulate matter emitted during forest fire. However, most previous studies quantify emissions of particulate matter and constituents using dry samples. In this study, we use a self-designed semi closed combustion simulator to quantify emission of total carbon (TC), organic carbon (OC), elemental carbon (EC) and water-soluble ions in fine particulate matter (PM_2.5) using fuels of four tree species that differ in moisture content (0, 10, 20 and 30%). The results showed that the emissions of TC, OC and EC and total water-soluble inorganic ions increased significantly (<0.05) with increasing moisture content of fuels, and fuels of coniferous species emitted significantly more pollutants than fuels of broadleaved species. Similarly combustion of leaf samples emitted more carbonaceous components and water-soluble ions than combustion of branches. K⁺, NH₄⁺ and Cl^- were the main components of water-soluble ionic species, and emissions of K⁺, Ca²⁺, Na⁺, Mg²⁺, NH₄⁺, Cl^-, Br^-, NO₃^-, NO₂^-, SO₄^2- increased with increasing moisture content of fuels. Fuel moisture content had a great impact on the inorganic salt composition in the particulate matter emitted during combustion. The findings have an important implication on the use of prescribed early fire as management tools as the moisture content of the fuels early during the dry season is still high.

Strategies to improve solid state anaerobic bioconversion of lignocellulosic biomass: an overview

Solid state anaerobic digestion (SSAD) of lignocellulosic biomass may be attractive solution for its valorisation. Compared to liquid state anaerobic digestion (LSAD), SSAD can handle higher organic loading rates (OLR), requires a less water and smaller reactor volume. It may require lower energy demand for heating or mixing and has higher volumetric methane productivity. Besides numerous benefits of SSAD processes and progress in system design, there are still obstacles, which need to be overcome for its successful implementations. This review aims to compile the recent trends in enhancing the bioconversion of agricultural stubbles in SSAD. Several pretreatment procedures used to breaking lignin and cellulose complex, method to overcome carbon to nitrogen ratio imbalance, use of carbon-based conducting materials to enhance Volatile Fatty Acids (VFA) conversion and additives for achieving nutrient balance will be discussed in this review. Leachate recirculation and its impacts on SSAD of agricultural stubbles are also discussed.

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

From late 2019 to early 2020, forest fires in southeastern Australia caused huge economic losses and huge environmental pollution. Monitoring forest fires has become increasingly important. A new method of fire detection using the difference between global navigation satellite system (GNSS)-derived precipitable water vapor and radiosonde-derived precipitable water vapor (ΔPWV) is proposed. To study the feasibility of the new method, the relationship is studied between particulate matter 10 (PM10) (2.5 to 10 microns particulate matter) and ΔPWV based on Global Positioning System (GPS) data, radiosonde data, and PM10 data from 1 June 2019 to 1 June 2020 in southeastern Australia. The results show that before the forest fire, ΔPWV and PM10 were smaller and less fluctuating. When the forest fire happened, ΔPWV and PM10 were increasing. Then after the forest fire, PM10 became small with relatively smooth fluctuations, but ΔPWV was larger and more fluctuating. Correlation between the 15-day moving standard deviation (STD) time series of ΔPWV and PM10 after the fire was significantly higher than that before the fire. This study shows that ΔPWV is effective in monitoring forest fires based on GNSS technique before and during forest fires in climates with more uniform precipitation, and using ΔPWV to detect forest fires based on GNSS needs to be further investigated in climates with more precipitation and severe climate change.