Variability in pyrogenic carbon properties generated by different burning temperatures and peatland plant litters: implication for identifying fire intensity and fuel types

被引:12
作者
Gao, Chuanyu [1 ]
Cong, Jinxin [1 ]
Sun, Yang [1 ]
Han, Dongxue [1 ]
Wang, Guoping [1 ]
机构
[1] Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Shengbei St 4888, Changchun 130102, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
carbon; fire history; fire intensity; FTIR; fuel; pyrogenic carbon; soil; stable carbon isotope; thermal stability; GREAT HINGGAN MOUNTAINS; AROMATIC CONDENSATION; BIOCHAR STABILITY; CLIMATE; WOOD; THERMOSEQUENCE; SEQUESTRATION; DECOMPOSITION; SPECTROSCOPY; DEPOSITION;
D O I
10.1071/WF21053
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Pyrogenic carbon (PyC), generated by fire, acts as a stable carbon deposit in natural ecosystems and is widely used to reconstruct fire history. Fuel type and burning temperature are the two major factors that influence PyC properties and exert variable effects on soil carbon pools, especially for peatlands. However, whether analysis of PyC can identify these two factors remains unclear. To address this knowledge gap, we selected typical peatland plant litters of seven shrub and seven herb plants in the Great Khingan Mountains, China. The properties of PyC produced at 250 degrees C (low-intensity burning) and 600 degrees C (high-intensity burning) without oxygen were evaluated. The results showed that the effects of burning temperature and plant type on delta C-1(3)-PyC were not significant. The differences in the initial compositions of herbs and shrubs led to more aromatic and carboxylic compounds in shrub PyC than in herb PyC. A high burning temperature led to less labile components (e.g. aliphatic compounds and acids) and higher thermal stability of high-temperature PyC compared to that of low-temperature PyC. Our results also indicate that several typical PyC chemical composition indicators (e.g. Fourier-transform infrared spectroscopy 1515/1050 ratio and 1720/1050 ratio) can potentially identify PyC sources.
引用
收藏
页码:395 / 408
页数:14
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