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.

Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

Composition of inorganic elements in fine particulate matter emitted during surface fire in relation to moisture content of forest floor combustibles.

The moisture content of combustible material on the forest floor is constantly changing due to environmental factors, which have a direct impact on the composition and emission intensity of particulate matter released during fire. In this study, an indoor biomass combustion analysis device was used to analyze the emission characteristics of fine particulate matter (PM2.5) from combustion of herbaceous combustible materials with different moisture contents (0%, 15%, and 30%). The composition of inorganic elements in PM2.5 (Zn, K, Mg, Ca, and other 13 measurable elements) were determined by inductively coupled plasma-mass spectrometer (ICP-MS). The results showed that the PM2.5 emission factor increased significantly with the increase of moisture content of combustible materials in the range of 11.63 ± 0.55 for dry samples to 36.71 ± 1.21 g/kg for samples with 30% moisture content. The main elemental components of PM2.5 were K, Zn, Ca, Mg, and Na and K, Ca, Mg, and Na emission factors increased with the increase of moisture content of combustibles. The proportion of macronutrients in PM2.5 released by combustion of each herb increased as the moisture content increased, but the proportion of trace elements gradually decreased. There was a good correlation between elemental composition of PM2.5 and that of herbaceous combustibles. The results provide evidence that the moisture content of combustible materials has a significant effect on the emission of inorganic elements in particulate matter, and hence cautions should be exercised during fuel reduction treatments, such as early prescribed fire.

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, PM2.5, 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. PM2.5, 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 PM2.5 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.

[Applicability of mixed effect model in the prediction of forest fire]. / 混合效应模型在林火发生预测中的适用性.

Fire is an important influencing factor in forest ecosystems. Establishing an accurate forest fire forecasting model is important for forest fire management. We used different meteorological factors as predictors to construct a forest fire prediction model in Fujian Province, based on Logistic regression and generalized linear mixed effect model. We compared the fitness and prediction accuracy of the two models, judged the applicability of the mixed effect model in forest fire forecasting. The results showed that the AUC and accuracy values of the Logistic base model were 0.664 and 60.4%, respectively. Models considering random effects gave better fitting and validating statistics. Among them, the two-level mixed model containing both area and altitude difference effects performed best, with increases of 0.057 and 6.0% for the AUC and accuracy values, respectively. By applying the model to predict the probability of forest fires in Fujian Province, we found that the middle-incidence and high-incidence areas of forest fires distributed in northwest and south Fujian, whereas the low-incidence areas of forest fires distributed in southwest and east Fujian, which was consistent with the observed data. The data fitting and forest fire prediction of the mixed effects model was better than those of the Logistic basic model. Therefore, it could be used as an important tool for forest fire prediction and management.

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 (PM2.5) 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-, SO42- 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.

[Seasonal variation and driving factors of forest fire in Zhejiang Province, China, based on MODIS satellite hot spots]. / 基于MODISå«æ˜Ÿç«ç‚¹çš„æµ™æ±Ÿçœæž—ç«å­£èŠ‚å˜åŒ–åŠé©±åŠ¨å› å­.

Understanding the changes and driving factors of forest fire can provide scientific basis for prevention and management of forest fire. In this study, we analyzed the changes and driving factors of forest fire in Zhejiang Province during 2001-2016 based on trend analysis and Logistic regression model with the MODIS satellite fire point data combined with meteorological (daily ave-rage wind speed, daily average temperature, daily relative humidity, daily temperature difference, daily cumulative precipitation), human activities (distance from road, distance from railway, distance from resident, population, per capita GDP), topographic and vegetation factors (elevation, slope, vegetation coverage). The results showed that the number of forest fires in spring and summer had significantly increased, while the forest fires in the autumn and winter increased first and then decreased. Forest fire in autumn significantly declined. The four seasons' fire occurrence prediction models had good prediction accuracy, reaching 75.8% (spring), 79.1% (summer), 74.7% (autumn) and 79.6% (winter). The meteorological, human activity, topographic and vegetation factors significantly affected fire occurrence in spring and summer, while meteorological factors were the main fire drivers in autumn and winter in Zhejiang. The focus of forest fire management should be on human activities. Fire prevention campaign should be done in spring and summer when high-risk forest fires were scattered in the study area. In autumn and winter, observatory and monitoring equipment could be built to facilitate fire management and detect in the area of high fire risk that was concentrated in the southwest region.

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

Data on emission of atmospheric pollutants at local scale is essential for accurately modelling forest fire emission at regional scale. In this study, we quantified emission factor (EF) of gaseous pollutants (CO, CO2, NOx, hydrocarbons, organic carbon, and inorganic elements), fine particulate matter (PM2.5), water soluble inorganic ions, and non-methane hydrocarbons (NMHCs) from leaves, branches and barks of five dominant tree species in Chinese boreal region. Results demonstrate that the emission factors of different pollutants varied among tree species and fuel typology. The average total EF (leaves + branches + barks) of different species ranged from 922 ± 116 mg/g to 1383 ± 134 mg/g for CO2; 225 ± 109 mg/g to 277 ± 21 mg/g for CO; 0.6 ± 0.2 mg/g to 3 ± 0.7 mg/g for NOx; 32 ± 6 mg/g to 55 ± 7 mg/g for hydrocarbons; 3 ± 0.3 mg/g to 6 ± 0.7 mg/g for organic carbon; 0.6 ± 0.1 mg/g to 2 ± 0.1 mg/g for elemental carbon; and 4 ± 0.7 mg/g to 12 ± 1 mg/g for PM2.5. The total water soluble ions ranged from 5 ± 0.6 mg/kg to 12 ± 1.3 mg/g. For most of the pollutants, combustion of barks emitted more than that of leaves and branches. A total of 48 types of NMHCs (19 alkanes, 15 alkenes, and 14 aromatic compounds) were released during combustion of leaves, barks, and branches of tree species, with EF ranged from 982 mg/g to 1375 mg/g. Alkenes and i-butane, 1-butene, 1,3-butadiene, Isoprene, 4-Methyl-1-pentene, p-Xylene and benzene were the major ozone-forming compounds. Our results provide a comprehensive emission data by species and fuel typology that can be useful for modelling climate change, source apportionment and atmospheric photochemistry.

[Forest fire management in the United States]. / 美国林火管理概况及分析.

With the intensification of climate change and human activities, megafires frequently occur, with serious impacts on ecosystems, atmospheric environment, and human health. The United States has accumulated a large amount of practical experience in forest fire management. A comprehensive review of the framework of forest fire management in the United States can provide an inspiring reference for forest fire prevention in China. Starting from the process of historical evolution of forest fire policy, we systematically introduced the four stages of policy evolution and the characteristics of each stage in the US. Moreover, a comprehensive analysis of forest fire management situation in the US from four aspects was conducted, including the management of combustible fuels, administrative responsibility, fire suppression and forest fire management research support. We summarized relevant literature and proposed improvement strategies for future combustibles management, policy politics and fire fighting in the United States. Through the comprehensive analysis of forest fire management in the United States, we put forward some inspiring opinions on forest fire management in China to promote the establishment of a sound forest fire management system with Chinese characteristics.

Dynamics of major air pollutants from crop residue burning in mainland China, 2000-2014.

Based on satellite image data and China's Statistical Yearbooks (2000 to 2014), we estimated the total mass of crop residue burned, and the proportion of residue burned in the field vs. indoors as domestic fuel. The total emissions of various pollutants from the burning of crop residue were estimated for 2000-2014 using the emission factor method. The results indicate that the total amount of crop residue and average burned mass were 8690.9Tg and 4914.6Tg, respectively. The total amount of emitted pollutants including CO2, CO, NOx, VOCs, PM2.5, OC (organic carbon), EC (element carbon) and TC (total carbon) were 4212.4-8440.9Tg, 192.8-579.4Tg, 4.8-19.4Tg, 18.6-61.3Tg, 18.8-49.7Tg, 6.7-31.3Tg, 2.3-4.7Tg, and 8.5-34.1Tg, respectively. The emissions of pollutants released from crop residue burning were found to be spatially variable, with the burning of crop residue mainly occurring in Northeast, North and South China. In addition, pollutant emissions per unit area (10 km × 10 km) were mostly concentrated in the central and eastern regions of China. Emissions of CO2, NOx, VOCs, OC and TC were mainly from rice straw burning, while burning of corn and wheat residues contributed most to emissions of CO, PM2.5 and EC. The increased ratio of PM2.5 emissions from crop residue burning to the total emitted from industry during the study period is attributed to the implementation of strict emissions management policies in Chinese industry. This study also provides baseline data for assessment of the regional atmospheric environment.

Understanding fire drivers and relative impacts in different Chinese forest ecosystems.

In this study, spatial patterns and driving factors of fires were identified from 2000 to 2010 using Ripley's K (d) function and logistic regression (LR) model in two different forest ecosystems of China: the boreal forest (Daxing'an Mountains) and sub-tropical forest (Fujian province). Relative effects of each driving factor on fire occurrence were identified based on standardized coefficients in the LR model. Results revealed that fires were spatially clustered and that fire drivers vary amongst differing forest ecosystems in China. Fires in the Daxing'an Mountains respond primarily to human factors, of which infrastructure is recognized as the most influential. In contrast, climate factors played a critical role in fire occurrence in Fujian, of which the temperature of fire season was found to be of greater importance than other climate factors. Selected factors can predict nearly 80% of the total fire occurrence in the Daxing'an Mountains and 66% in Fujian, wherein human and climate factors contributed the greatest impact in the two study areas, respectively. This study suggests that different fire prevention and management strategies are required in the areas of study, as significant variations of the main fire-driving exist. Rapid socio-economic development has produced similar effects in different forest ecosystems within China, implying a strong correlation between socio-economic development and fire regimes. It can be concluded that the influence of human factors will increase in the future as China's economy continues to grow - an issue of concern that should be further addressed in future national fire management.

Effects of fire disturbance on soil respiration in the non-growing season in a Larix gmelinii forest in the Daxing'an Mountains, China.

In boreal forests, fire is an important part of the ecosystem that greatly influences soil respiration, which in turn affects the carbon balance. Wildfire can have a significant effect on soil respiration and it depends on the fire severity and environmental factors (soil temperature and snow water equivalent) after fire disturbance. In this study, we quantified post-fire soil respiration during the non-growing season (from November to April) in a Larix gmelinii forest in Daxing'an Mountains of China. Soil respiration was measured in the snow-covered and snow-free conditions with varying degrees of natural burn severity forests. We found that soil respiration decreases as burn severity increases. The estimated annual C efflux also decreased with increased burn severity. Soil respiration during the non-growing season approximately accounted for 4%-5% of the annual C efflux in all site types. Soil temperature (at 5 cm depth) was the predominant determinant of non-growing season soil respiration change in this area. Soil temperature and snow water equivalent could explain 73%-79% of the soil respiration variability in winter snow-covering period (November to March). Mean spring freeze-thaw cycle (FTC) period (April) soil respiration contributed 63% of the non-growing season C efflux. Our finding is key for understanding and predicting the potential change in the response of boreal forest ecosystems to fire disturbance under future climate change.

Soil Respiration of the Dahurian Larch (Larix gmelinii) Forest and the Response to Fire Disturbance in Da Xing'an Mountains, China.

Despite the high frequency of wildfire disturbances in boreal forests in China, the effects of wildfires on soil respiration are not yet well understood. We examined the effects of fire severity on the soil respiration rate (Rs) and its component change in a Dahurian Larch (Larix gmelinii) in Northeast China. The results showed that Rs decreased with fire burning severity. Compared with the control plots, Rs in the low burning severity plots decreased by 19%, while it decreased by 28% in the high burning severity plots. The Rs decrease was mainly due to a decreased autotrophic respiration rate (Ra). The temperature sensitivity (Q 10) of Rs increased after the low severity fire disturbances, but it decreased after the high severity fire disturbance. The Rs were triggered by the soil temperature, which may explain most of the Rs variability in this area. Our study, for the first time, provides the data-based foundation to demonstrate the importance of assessing CO2 fluxes considering both fire severity and environmental factors post-fire in boreal forests of China.

[Prediction model of human-caused fire occurrence in the boreal forest of northern China].

The Chinese boreal forest is an important forest resource in China. However, it has been suffering serious disturbances of forest fires, which were caused equally by natural disasters (e.g., lightning) and human activities. The literature on human-caused fires indicates that climate, topography, vegetation, and human infrastructure are significant factors that impact the occurrence and spread of human-caused fires. But the studies on human-caused fires in the boreal forest of northern China are limited and less comprehensive. This paper applied the spatial analysis tools in ArcGIS 10.0 and Logistic regression model to investigate the driving factors of human-caused fires. Our data included the geographic coordinates of human-caused fires, climate factors during year 1974-2009, topographic information, and forest map. The results indicated that distance to railway (x1) and average relative humidity (x2) significantly impacted the occurrence of human-caused fire in the study area. The logistic model for predicting the fire occurrence probability was formulated as P= 1/[11+e-(3.026-0.00011x1-0.047x2)] with an accuracy rate of 80%. The above model was used to predict the monthly fire occurrence during the fire season of 2015 based on the HADCM2 future weather data. The prediction results showed that the high risk of human-caused fire occurrence concentrated in the months of April, May, June and August, while April and May had higher risk of fire occurrence than other months. According to the spatial distribution of possibility of fire occurrence, the high fire risk zones were mainly in the west and southwest of Tahe, where the major railways were located.

[Selection of advantage prediction model for forest fire occurrence in Tahe, Daxing'an Mountain].

This study chose zero-inflated model and Hurdle model that have been widely used in economic and social fields to model the fire occurrence in Tahe, Daxing'an Mountain. The AIC, LR and SSR were used to compare the models including zero-inflated Poisson model (ZIP), zero-inflated negative binomial model (ZINB), Poisson-Hurdle model (PH) and negative Binomial Hurdle (NBH) (two types, four models in total) so as to determine a better-fit model to predict the local fire occurrence. The results illustrated that ZINB model was superior over the other three models (ZIP, PH and NBH) based on the result of AIC and SSR tests. LR test revealed that the negative binomial distribution was suitable to both the "count" portion of zero-inflated model and hurdle model. Furthermore, this paper concluded that the zero-inflated model could better fit the fire feature of the study area according to the hypotheses of the two types of models.

[Applicability of different models in simulating the relationships between forest fire occurrence and weather factors in Daxing' an Mountains].

The Poisson's and Zero Inflated Poisson (ZIP) models that meet the data structure of forest fire occurrence were used to explore the relationships between the forest fire occurrence and climate factors in Daxing' an Mountains in 1980-2005. Compared with the ordinary least squares (OLS) model which often produced poor fitting results (R2 = 0.215), the Poisson's and ZIP models operated better, and had better prediction ability on the forest fire occurrence. The AIC and Vuong tests further indicated that ZIP model produced better fitting results, and thus, had better prediction ability than Poisson model.

[Estimation of total carbon-containing gas emission from main tree species in forest fires in Daxing' an mountains].

By the method of emission factor (EF), this paper estimated the total carbon-containing gas emission from five main tree species in Daxing' an Mountains in forest fires from 1980 to 2005. The results showed that different tree species had different EF. Pinus sylvesstris var. mongolica and Populus davidiana had the maximum and minimum EF of CO2, respectively. Larix gmelinii and Betula platyphylla had the maximun EF of CO and C(x)H(y), while B. platyphylla and L. gmelinii had the minimum EF of CO and C(x)H(y). Based on the carbon storage in different organs and the total biomass of the tree, it was estimated that the total emission of CO2, CO and C(x)H(y) from the five tree in the 25 years was 16.58 Tg, 1.61 Tg and 0.54 Tg, and the contributions of L. gmelinii, P. sylvesstris var. mongolica, B. platyphylla, P. davidiana, and Quercus mongolica were 5.00 Tg, 0.63 Tg and 0.05 Tg, 0.225 Tg, 0.023 Tg and 0.003 Tg, 11.22 Tg, 0.83 Tg and 0.41 Tg, 0.0022 Tg, 0.004 Tg and 0.00034 Tg, and 3.12 Tg, 0.13 Tg and 0.062 Tg, respectively.