摘要
Inertinite,as an important and abundant maceral group in coal,is critical for the study of palaeowildfires and their roles in the Earth’s ecosystems.Recently,there has been a significant amount of research on the relationship between palaeowildfire,palaeoclimate change and palaeovegetation evolution based on inertinite data.The reflectance of fusinite and semifusinite has been used to estimate the combustion temperature and type of palaeowildfires,and then to evaluate the combustion characteristics of different types of palaeowildfires.The relative abundance of inertinite can be used to estimate the atmospheric oxygen contents.The rapid development of artificial intelligence(AI)and online tools to search scientific databases has presented an opportunity for us to find,collect,arrange,and analyse data from the earliest to latest publications on inertinite.The data extraction tool Deep Shovel is used to collect and analyse global inertinite data from the Silurian to the Neogene.The software programs such as Gplates,ArcGIS pro and Tableau are then applied to model the relative abundance of inertinite over geological time,which can be correlated with other parameters such as atmospheric oxygen contents,plant evolution and palaeoclimate changes.The distribution of inertinite in coals varied over different geological periods,being typified by the“high inertinite content-high atmospheric oxygen level”period in the Permian and the“low inertinite content-low atmospheric oxygen level”period in the Cenozoic.This study has proposed a possible model of the positive and negative feedbacks between inertinite characteristics and palaeoenvironmental factors,and has revealed the exceptional role of inertinite in palaeoenvironmental studies.Future research on inertinite will be focused on the integrated study of organic petrology and organic geochemistry of inertinite,the big data-driven research on the temporal and spatial distribution of the global inertinite,the exploration of the functions of palaeowildfires in the Earth systems in different climatic backgrounds,and the study of modern wildfires to better predict the future frequency and intensity of wildfires due to climate changes.
基金
supported by the National Natural Science Foundation of China(Grant Nos.42321002,41572090)
the Deep-time Digital Earth(DDE)Big Science Program。
