红松人工林地表可燃物燃烧释放颗粒物质量及影响因素

doi:10.11707/j.1001-7488.20220311

摘要/Abstract

摘要：

目的: 基于燃烧风洞实验室进行室内点烧试验, 定量揭示红松人工林地表可燃物燃烧释放颗粒物粒径分布及变化特征, 为森林火灾释放的颗粒物提供参考。方法: 选取东北东部山区红松人工林为对象, 构造不同风速、可燃物载量、可燃物含水率的可燃物床层。基于燃烧风洞实验室进行108次点烧试验, 利用溶气胶监测仪(美国TSI Dust Trak 8533)进行实时监测, 通过随机森林算法建立不同粒径颗粒物预测模型。结果: 风速是影响4种粒径颗粒物质量最主要因素之一, PM₁受风速(37.207%)和温度(25.651%)影响最大, 受可燃物含水率影响最小(8.304%); PM_2.5受风速(43.293%)和可燃物载量(22.855%)影响最大, 受燃烧效率(7.509%)影响最小; PM₄受风速(43.552%)和可燃物载量(21.225%)影响最大, 受可燃物含水率(6.841%)影响最小; PM₁₀受风速(40.832%)和可燃物载量(23.337%)影响最大, 受可燃物含水率(6.946%)影响最小。基于随机森林算法构建的PM₁、PM_2.5、PM₄、PM₁₀预测模型R²分别为0.804、0.810、0.806和0.812。结论: 颗粒物质量与风速、可燃物载量、可燃物含水率、燃烧效率(＞80%)呈正相关, 与温度和相对湿度呈负相关。总体而言, 随机森林算法能够较好地解析各变量与颗粒物质量之间的复杂关系。红松人工林地表可燃物燃烧释放颗粒物观测区间1.72~56.04 g, 预测变化区间为5.67~36.33 g, 可为污染源排放清单的建立、消防从业人员的职业暴露标准提供数据基础。

关键词: 红松人工林, 燃烧, 颗粒物, 风速, 随机森林

Abstract:

Objective: Based on the indoor burning experiment carried out in the combustion wind tunnel laboratory, the particle size distribution and variation characteristics of the particulate matter released by surface fuel combustion in Korean pine plantation were quantitatively revealed, which would provide reference for the particulate matter released by forest fire. Method: The Korean pine plantation in the eastern mountainous area of Northeast China was selected as the object, and the fuel bed with different wind speed, fuel load and fuel moisture content was constructed. Based on 108 ignition experiments in the combustion wind tunnel laboratory, real-time monitoring was carried out by using the dissolved aerosol monitor (TSI Dust Trak 8533, USA), and random forest algorithm was used to establish a prediction model for particles of different sizes. Result: Wind speed was one of the most important factors affecting the mass of the four particle matter sizes. PM₁ was most affected by wind speed (37.207%) and temperature (25.651%), and was least affected by fuel moisture content (8.304%); PM_2.5 was most affected by wind speed (43.293%) and fuel load (22.855%), and was least affected by combustion efficiency (7.509%); PM₄ was the most affected by wind speed (43.552%) and fuel load (21.225%), and was least affected by fuel moisture content (6.841%); PM₁₀ was most affected by wind speed (40.832%) and fuel load (23.337%), and was least affected by fuel moisture content (6.946%). The R² of prediction models for PM₁、PM_2.5、PM₄、PM₁₀ based on the random forest algorithm were 0.804, 0.810, 0.806 and 0.812, respectively. Conclusion: The mass of particulate matter is positively correlated with wind speed, fuel load, fuel moisture content, and combustion efficiency (>80%), and negatively correlated with temperature and relative humidity. In general, the random forest algorithm can be used to better analyze the complex relationships between various variables and the mass of particulate matter. The observed range of particulate matter released from surface fuel combustion in Korean pine plantation is 1.72-56.04 g, and the predicted range is 5.67-36.33 g, which provides a data basis for the establishment of pollution source emission inventories and occupational exposure standards for fire-fighting practitioners.

Key words: Korean pine plantation, combustion, particulates, wind speed, random forest

中图分类号:

ST18.5
S762

刘鑫源,杨光,宁吉彬,耿道通,于宏洲,邸雪颖. 红松人工林地表可燃物燃烧释放颗粒物质量及影响因素[J]. 林业科学, 2022, 58(3): 97-106.

Xinyuan Liu,Guang Yang,Jibin Ning,Daotong Geng,Hongzhou Yu,Xueying Di. Quality and Influencing Factors of Particulate Matter Released by Surface Fuel Combustion in Korean Pine Plantation[J]. Scientia Silvae Sinicae, 2022, 58(3): 97-106.

图/表 6

表1

表2

图1

图2

图3

图4

参考文献 0

	陈亮, 周国模, 杜华强, 等. 基于随机森林模型的毛竹林CO₂通量模拟及其影响因子. 林业科学, 2018, 54 (8): 1- 12.
	Chen L , Zhou G M , Du H Q , et al. Simulation of CO₂ flux and controlling factors in moso bamboo forest using random forest algorithm. Scientia Silvae Sinicae, 2019, 54 (8): 1- 12.
	郭林飞, 马远帆, 郭新彬, 等. 不同燃烧状态下大兴安岭主要乔灌树种碳排放分析. 福建农林大学学报(自然科学版), 2020, 49 (4): 524- 531.
	Guo L F , Ma Y F , Guo X B , et al. Analysis of the carbon emission of main tree species in Daxing'an Mountain under different burning conditions. Journal of Fujian Agriculture and Forestry University (Natural Science Edition), 2020, 49 (4): 524- 531.
	侯俊雄, 李琦, 朱亚杰, 等. 基于随机森林的PM_2.5实时预报系统. 测绘科学, 2017, 42 (1): 1- 6.
	Hou J X , Li Q , Zhu Y J , et al. Real-time forecasting system of PM_2.5 concentration based on spark framework and random forest model. Science of Surveying and Mapping, 2017, 42 (1): 1- 6.
	胡海清, 罗碧珍, 魏书精, 等. 1953—2011年小兴安岭森林火灾含碳气体排放的估算. 应用生态学报, 2013, 24 (11): 3065- 3076.
	Hu H Q , Luo B J , Wei S J , et al. Estimation of carbonaceous gases emission from forest fires in Xiao Xing'an Mountains of Northeast China in 1953-2011. Chinese Journal of Applied Ecilogy, 2013, 24 (11): 3065- 3076.
	鞠园华, 马祥庆, 郭林飞, 等. 杉木枯落物燃烧释放污染物特征及PM_2.5成分分析. 林业科学, 2019, 55 (7): 187- 196.
	Ju Y H , Ma X Q , Guo L F , et al. Characteristics of pollutants released by combustion of Chinese fir litterfalll and PM_2.5 composition analysis. Scientia Silvae Sinicae, 2019, 55 (7): 187- 196.
	梁慧玲, 林玉蕊, 杨光, 等. 基于气象因子的随机森林算法在塔河地区林火预测中的应用. 林业科学, 2016, 52 (1): 89- 98. doi: 10.3969/j.issn.1006-1126.2016.01.017
	Liang H L , Lin Y R , Yang G , et al. Application of random forest algorithm on the forest fire prediction in Tahe area based on meteorolo factors. Scientia Silvae Sinicae, 2016, 52 (1): 89- 98. doi: 10.3969/j.issn.1006-1126.2016.01.017
	吴丹, 曹双, 汤莉莉. 南京北郊大气颗粒物的粒径分布及其影响因素分析. 环境科学, 2016, 37 (9): 3268- 3279.
	Wu D , Cao S , Tang L L , et al. Variation of size distribution and the influnecing factors of aerosol in northern suburbs of Nanjing. Environmental Science, 2016, 37 (9): 3268- 3279.
	杨光, 张远艳, 邸雪颖, 等. 蒙古栎床层燃烧排放PM_2.5及影响因子. 东北林业大学学报, 2018, 46 (11): 66- 69.
	Yang G , Zhang Y Y , Di X Y , et al. Influence factors on PM_2.5 emission from Qurecus mongolica broad leaves fuel bed burning. Journal of Northeast Forestry University, 2018, 37 (9): 3268- 3279.
	张吉利, 刘礴霏, 邸雪颖, 等. 平地无风条件下蒙古栎阔叶床层的火行为Ⅲ. 火线强度、可燃物消耗和燃烧效率分析及预测模型. 应用生态学报, 2013, 24 (12): 3381- 3390.
	Zhang J L , Liu B F , Di X Y , et al. Fire behavior of Quercus mongolica leaf litter fuelbed under zero-slope and no-wind condictions Ⅲ. Analysis and modeling of fireline intensity, fuel consumption, and combustion efficiency. Chinese Journal of Applied Ecoilogy, 2013, 24 (12): 3381- 3390.
	张雷, 孙鹏森, 刘世荣. 川西亚高山森林不同恢复阶段生长季蒸腾特征. 林业科学, 2020, 56 (1): 1- 9. doi: 10.3969/j.issn.1006-1126.2020.01.001
	Zhang L , Sun P S , Liu S R , et al. Growing-season transpiration of typical forests in different succession stages in subalpine region of western Sichuan, China. Scientia Silvae Sinicae, 2020, 56 (1): 1- 9. doi: 10.3969/j.issn.1006-1126.2020.01.001
	张远艳, 邸雪颖, 赵凤君, 等. 红松人工林地表针叶可燃物燃烧PM_2.5排放影响因子. 北京林业大学学报, 2018, 40 (6): 30- 40.
	Zhang Y Y , Di X Y , Zhao F J , et al. Influencing factors of PM_2.5 emission under the surface needle combustible combustion of Korean pine plantations. Journal of Beijing Forestry University, 2018, 40 (6): 30- 40.
	Adetona O , Simpson C D , Onstad G , et al. Exposure of wildland firefighters to carbon monoxide, fine particles, and levoglucosan. The Annals of Occupational Hygiene, 2013, 57 (8): 979- 991.
	Aguilera R C T G . Wildfire smoke impacts respiratory health more than fine particles from other sources: Observational evidence from southern California. Nature Communications, 2021, 12, 1493. doi: 10.1038/s41467-021-21708-0
	Averett N . Smoke signals: Teasing out adverse health effects of wildfire emissions. Environmental Health Perspectives, 2016, 124 (9): A166.
	Breiman L . Random forest. Machine Learning, 2001, 45, 5- 32. doi: 10.1023/A:1010933404324
	Chen L , Shi M , Li S , et al. Quantifying public health benefits of environmental strategy of PM_2.5 air quality management in Beijing-Tianjin-Hebei Region, China. Journal of Environmental Sciences, 2017, 57 (7): 33- 40.
	Chen Z Y , Chen D L , Zhao C F , et al. Influence of meteorological conditions on PM_2.5 concentrations across China: A review of methodology and mechanism. Environment International, 2020, 139, 105558. doi: 10.1016/j.envint.2020.105558
	Delgado-Baquerizo M , Maestre F T , Reich P B , et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nature Communications, 2016, 28 (7): 10541.
	Dong T T , Stock W D , CALLAN A C , et al. Emission factors and composition of PM_2.5 from laboratory combustion of five western Australian vegetation types. Science of the Total Environment, 2020, 703, 134796. doi: 10.1016/j.scitotenv.2019.134796
	Forehead H , Barthelemy J , Arshad B , et al. Traffic exhaust to Wildfires: PM_2.5 measurements with fixed and portable, low-cost LoRaWAN-connected Sensors. PLoS One, 2020, 15 (4): e231778.
	Guo F , Wang G , Su Z , et al. What drives forest fire in Fujian, China? Evidence from logistic regression and random forests. International Journal of Wildland Fire, 2016, 25 (5): 505- 519. doi: 10.1071/WF15121
	Hou X , Fei D , Kang H , et al. Seasonal statistical analysis of the impact of meteorological factors on fine particle pollution in China in 2013-2017. Natural Hazards, 2018, 93 (2): 677- 698. doi: 10.1007/s11069-018-3315-y
	Johnson M C , Halofsky J E , Peterson D L . Effects of salvage logging and pile-and-burn on fuel loading, potential fire behaviour, fuel consumption and emissions. International Journal of Wildland Fire, 2013, 22 (6): 757- 769. doi: 10.1071/WF12080
	Kaminska J A . A random forest partition model for predicting NO₂ concentrations from traffic flow and meteorological Conditions. Science of the Total Environment, 2019, 651, 475- 483. doi: 10.1016/j.scitotenv.2018.09.196
	Karanasiou A , Alastuey A , Amato F , et al. Short-term health effects from outdoor exposure to biomass burning emissions: A review. Science of the Total Environment, 2021, 781, 146739. doi: 10.1016/j.scitotenv.2021.146739
	Kayes I , Shahriar S A , Hasan K , et al. The relationships between meteorological parameters and air pollutants in an urban environment. Global Journal of Environmental Science and Management, 2019, 5 (3): 265- 278.
	Leonard S S , Castranova V , Chen B T , et al. Particle size-dependent radical generation from wildland fire smoke. Toxicology, 2007, 236 (1): 103- 113.
	Liao T T , Wang S , Ai J , et al. Heavy pollution episodes, transport pathways and potential sources of PM_2.5 During the winter of 2013 in Chengdu (China). Science of the Total Environment, 2017, 584-585, 1056- 1065. doi: 10.1016/j.scitotenv.2017.01.160
	Liu F , Wang X C , Wang C K , et al. Environmental and biotic controls on the interannual variations in CO₂ fluxes of a continental monsoon temperate forest. Agriculturaland Forest Meteorology, 2021, 296, 108232. doi: 10.1016/j.agrformet.2020.108232
	Lu X , Yao T , Fung J C H , et al. Estimation of health and economic costs of air pollution over the Pearl River Delta Region in China. Science of the Total Environment, 2016, 566-567, 134- 143. doi: 10.1016/j.scitotenv.2016.05.060
	Ma Y , Tigabu M , Guo X , et al. Water-soluble inorganic ions in fine particulate emission during forest fires in Chinese boreal and subtropical Forests: An indoor experiment. Forests, 2019, 10 (11): 994. doi: 10.3390/f10110994
	Masinda M M , Li F , Liu Q , et al. Prediction model of moisture content of dead fine fuel in forest plantations on Maoer Mountain, Northeast China. Journal of Forestry Research, 2021, 32 (5): 2023- 2035. doi: 10.1007/s11676-020-01280-x
	Mehmood K . Spatial and temporal distributions of air pollutant emissions from open crop straw and biomass burnings in China from 2002 to 2016. Environmental Chemistry Letters, 2018, 1 (16): 301- 309.
	NASA. Explosive Fire Activity in Australia. (2021-04-07). https://earthobservatory.nasa.gov/images/146125/explosive-fire-activity-in-australia.
	Ni H , Han Y , Cao J , et al. Emission characteristics of carbonaceous particles and trace gases from open burning of crop residues in China. Atmospheric Environment, 2015, 123, 399- 406. doi: 10.1016/j.atmosenv.2015.05.007
	Qiu D , Liu J , Zhu L , et al. Particulate matter assessment of a wetland in Beijing. Journal of Environmental Sciences, 2015, 36, 93- 101. doi: 10.1016/j.jes.2015.04.016
	Stirnberg R , Cermak J , Kotthaus S , et al. Meteorology-driven variability of air pollution (PM₁) revealed with explainable machine learning. atmospheric Chemistry & Physics, 2020, 3919- 3948.
	Sun P , Zhang Y , Su nL , et al. Influence of fuel moisture content, packing ratio and wind velocity on the ignition probability of fuel beds composed of Mongolian oak leaves via cigarette butts. Forests, 2018, 9 (9): 507. doi: 10.3390/f9090507
	Tai A P K , Mickley L J , Jacob D J . Correlations between fine particulate matter (PM_2.5) and meteorological variables in the United states: Implications for the sensitivity of PM_2.5 to climate change. Atmospheric Environment, 2010, 44 (32): 3976- 3984. doi: 10.1016/j.atmosenv.2010.06.060
	Virgilio G D , Hart M A , Jiang N . Meteorological controls on atmospheric particulate pollution during hazard reduction burns. Atmospheric Chemistry & Physics, 2018, 18 (9): 1- 37.
	Wang J , Susumu O . Effects of meteorological conditions On PM_2.5 Concentrations in Nagasaki, Japan. International Journal of Environmental Research and Public Health, 2015, 12 (8): 9089- 9109. doi: 10.3390/ijerph120809089
	WHO. 2016. Ambient Air Pollution: A global assessment of exposure and burden of disease. World Health Organisation.
	Wu Z , He H S , Yang J , et al. Relative effects of climatic and local factors on fire occurrence in boreal forest landscapes of Northeastern China. Science of the Total Environment, 2014, 493, 472- 480. doi: 10.1016/j.scitotenv.2014.06.011
	Yang X , Ma Y , Wang G , et al. Characterization of pollutants emitted during burning of eight main tree species in subtropical China. Atmospheric Environment, 2019, 215 (Oct.): 116891- 116899.
	Yin Z , Wang H , Chen H . Understanding severe winter haze events in the north China Plain in 2014: Roles of climate anomalies. Atmospheric Chemistry & Physics, 2017, 17 (3): 1- 27.
	Wegesser T C , Pinkerton K E , Last J A . California wildfires of 2008: Coarse and fine particulate matter toxicity. Environmental Health Perspectives, 2009, 117 (6): 893- 897. doi: 10.1289/ehp.0800166

样地编号 Sample No.	平均胸径 Mean DBH /cm	平均树高 Average height/m	林龄 Forest age/a	造林密度 Planting density	郁闭度 Canopy density	可燃物载量 Fuel load/(t·hm^-2)
样地编号 Sample No.	平均胸径 Mean DBH /cm	平均树高 Average height/m	林龄 Forest age/a	造林密度 Planting density	郁闭度 Canopy density	平均值 Average	A	B	C	D	E
1	22.7±0.48	20.3±0.78	44	2m×2m	0.6	3.4	4.5	3.3	3.2	2.1	3.9
2	26.9±0.56	24.3±0.51	44	2m×2m	0.8	6.3	8.1	5.6	4.3	3.5	10.0
3	18.2±0.12	13.7±0.06	44	2m×2m	0.7	6.8	5.4	10.2	6.6	6.9	4.9

模型变量 Vari ables of model	最小值 Min	最大值 Max	均值±标准误差 Mean SE	百分位数Percentile
模型变量 Vari ables of model	最小值 Min	最大值 Max	均值±标准误差 Mean SE	25%	50%	75%
温度Temperature /℃	5.10	17.60	12.18±0.32	9.55	15.07	17.60
相对湿度Relativehumidity (%)	32.00	94.10	66.31±1.65	54.13	81.58	94.10
风速Wind speed /(m·s^-1)	0.00	3.00	1.50±0.11	0.25	2.75	3.00
预设含水率Preset moisture content (%)	5.00	15.00	9.95±0.38	5.00	10.00	15.00
实际含水率Actual moisture content (%)	2.60	15.38	9.41±0.39	5.02	14.03	15.37
可燃物载量Fuel load /(t·hm^-2)	6.00	10.00	8.00±0.16	6.00	8.00	10.00
燃烧效率Combustion efficiency(%)	33.19	99.27	85.32±0.72	82.99	89.33	99.27
PM₁质量PM₁ mass/g	1.72	55.45	16.63±1.07	8.25	13.68	22.07
PM_2.5质量PM_2.5 mass /g	1.73	55.96	16.78±1.08	8.35	13.78	22.36
PM₄质量PM₄mass/g	1.73	56.00	16.82±1.08	8.38	13.81	22.55
PM₁₀质量PM₁₀ mass /g	1.79	56.04	16.91±1.08	8.49	13.98	22.63

[1]	孙忠秋,高金萍,吴发云,高显连,胡杨,高剑新. 基于机载激光雷达点云和随机森林算法的森林蓄积量估测[J]. 林业科学, 2021, 57(8): 68-81.
[2]	宁吉彬,耿道通,于宏洲,邸雪颖,杨光. 基于Logistic回归的兴安落叶松林飞火引燃试验[J]. 林业科学, 2021, 57(7): 121-130.
[3]	由珈齐,李明泽,范文义,全迎,王斌,莫祝坤,祝子枭. 基于高光谱和激光雷达数据的林分类型识别[J]. 林业科学, 2021, 57(5): 119-129.
[4]	潘颖,丁鸣鸣,林杰,代侨,郭赓,崔琳琳. 基于PROSAIL模型和多角度遥感数据的森林叶面积指数反演[J]. 林业科学, 2021, 57(4): 90-106.
[5]	李文博,吕振刚,黄选瑞,张志东. 河北省北部华北落叶松人工林立地指数空间分布预测[J]. 林业科学, 2021, 57(3): 79-89.
[6]	丁家祺,黄文丽,刘迎春,胡杨. 基于机器学习和多源数据的湘西北森林地上生物量估测[J]. 林业科学, 2021, 57(10): 36-48.
[7]	马煦,曹治国,岳晨,金楚晗,刘俊,刘洋,修桂芳,席本野. 降雨和灌溉影响下毛白杨叶片的颗粒物滞纳特征变化及其生理特性响应规律[J]. 林业科学, 2020, 56(8): 181-190.
[8]	刘延惠,侯贻菊,舒德远,杨冰,崔迎春,丁访军. 贵阳市主要绿化树种叶面吸滞颗粒物特征及其时空变化[J]. 林业科学, 2020, 56(6): 12-25.
[9]	张少伟,岳晨,詹振枫,段劼,曹治国,刘俊祥,古琳. 4种柳树叶片表面易去除与难去除颗粒物滞纳特征[J]. 林业科学, 2020, 56(6): 26-34.
[10]	郑文霞,郭新彬,郭雨萱,曾爱聪,魏帽,郭林飞,马远帆,郭福涛. 大兴安岭典型乔木树种燃烧排放非甲烷总烃(NMHCs)的特性[J]. 林业科学, 2020, 56(12): 101-113.
[11]	张雷, 孙鹏森, 刘世荣. 川西亚高山森林不同恢复阶段生长季蒸腾特征[J]. 林业科学, 2020, 56(1): 1-9.
[12]	侯晓静, 明金科, 秦荣水, 朱霁平. 基于随机森林模型的交界域火灾风险分析[J]. 林业科学, 2019, 55(8): 194-200.
[13]	鞠园华, 马祥庆, 郭林飞, 马远帆, 蔡奇均, 郭福涛. 杉木枯落物燃烧释放污染物特征及PM_2.5成分分析[J]. 林业科学, 2019, 55(7): 187-196.
[14]	兰洁,雷相东,张毓涛. 天山中部雪岭云杉林多功能权衡与协同关系[J]. 林业科学, 2019, 55(11): 9-18.
[15]	张英凯,刘鹏举,刘长春,任怡. 基于空间聚类的杉木生长预测方法[J]. 林业科学, 2019, 55(11): 137-144.