Recognition of peat depositional environments in coal: A review

Dai, Shifeng; Bechtel, Achim; Eble, Cortland F.; Flores, Romeo M.; French, David; Graham, Ian T.; Hood, Madison M.; Hower, James C.; Korasidis, Vera A.; Moore, Tim A.; Püttmann, Wilhelm; Wei, Qiang; Zhao, Laishi; O’Keefe, Jennifer M.K.

doi:10.1016/j.coal.2019.103383

Cited by 299 publications

(191 citation statements)

References 717 publications

Supporting

Mentioning

190

Contrasting

Unclassified

Order By: Relevance

“…Such tripartite divisions appear to be common in coastal plain (paralic) coals and have been described and interpreted in terms of environment and vegetation first from the Carboniferous of Britain [72–74]. Mechanisms controlling facies and environment during transgression and regression in peat forming paralic domains have recently been reviewed by [75]. Seam 1 is sandwiched between Interbeds I (PZ 1) and II (PZ 5), both showing marine influence and being largely separated from the PZs 2 to 4 in the NMDS of the total data set (Fig 13).…”

Section: Discussionmentioning

confidence: 99%

Greenhouse conditions in lower Eocene coastal wetlands? – Lessons from Schöningen, Northern Germany

Lenz

Riegel

Wilde

2020

Preprint

View full text Add to dashboard Cite

The Paleogene succession of the Helmstedt Lignite Mining District in Northern Germany includes coastal peat mire records from the latest Paleocene to the middle Eocene at the southern edge of the Proto-North Sea. Therefore, it covers the different long- and short-term climate perturbations of the Paleogene greenhouse. 56 samples from three individual sections of a Lower Eocene seam in the record capture the typical succession of the vegetation in a coastal wetland during a period that was not affected by climate perturbation. This allows to distinguish facies-dependent vegetational changes from those that were climate induced. Cluster analyses and NMDS of well-preserved palynomorph assemblages reveal four successional stages in the vegetation during peat accumulation: (1) a near-coastal vegetation, (2) a lowland mire, (3) a transitional mire, and (4) a terminal mire. Biodiversity measures show that plant diversity decreased significantly in the successive stages. The highly diverse vegetation at the coast and in the adjacent lowland mire was replaced by low diversity communities adapted to wet acidic environments and nutrient deficiency. The palynomorph assemblages are dominated by elements such as Alnus or Sphagnum. Typical tropical elements which are characteristic for the Middle Eocene part of the succession are missing. This indicates that a more temperate climate prevailed in northwestern Germany during the early Lower Eocene.

show abstract

Section: Discussionmentioning

confidence: 99%

Greenhouse conditions in lower Eocene coastal wetlands? – Lessons from Schöningen, Northern Germany

Lenz

Riegel

Wilde

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The coals deposited in the marine-influenced environments, such as tidal flat environments of restricted carbonate platforms [5,19,78,79], and some non-marine-influenced environments such as brackish peat formed in the distributary channel-delta front, beach bar and interdistributary bay [24], are enriched in B. In addition, hydrothermal fluids, volcanic activity, acidic water, and climatic variation also elevated the concentration of B [80][81][82][83][84][85][86][87].…”

Section: Diagenetic Fluid Suppliesmentioning

confidence: 99%

Geological Controls on Mineralogy and Geochemistry of the Permian and Jurassic Coals in the Shanbei Coalfield, Shaanxi Province, North China

Shangguan

Zhuang

et al. 2020

Minerals

View full text Add to dashboard Cite

Coal as the source of critical elements has attracted much attention and the enrichment mechanisms are of significant importance. This paper has an opportunity to investigate the mineralogical and geochemical characteristics of the Permian and Jurassic bituminous coals and associated non-coals from two underground coal mines in the Shanbei Coalfield (Northeast Ordos basin), Shaanxi Province, North China, based on the analysis of X-ray diffraction (XRD), inductively coupled plasma atomic-emission spectrometry (ICP-AES/MS), and scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM-EDS). The Jurassic and Permian coals have similar chemical features excluding ash yield, which is significantly higher in the Permian coals. Major mineral matters in the Jurassic coals are quartz, kaolinite, and calcite. By contrast, mineral assemblages of the Permian coals are dominated by kaolinite; and apatite occurring in the middle section’s partings. The Jurassic coals are only enriched in B, whereas the Permian coals are enriched in some trace elements (e.g., Nb, Ta, Th, and REY). Boron has a mixed inorganic and organic association which may be absorbed by organic matter from fluid (or groundwater) or inherited from coal-forming plants. Additionally, climatic variation also plays an important role. As for the Permian coals, kaolinite and apatite as the major carriers of elevated elements; the former were derived from the sedimentary source region (the Yinshan Oldland and the Benxi formation) and later precipitated from Ca-, and P-rich solutions. We deduced that those elevated elements may be controlled by the source rock and diagenetic fluid input. The findings of this work offered new data to figure out the mechanism of trace element enrichment of coal in the Ordos basin.

show abstract

“…Here, x j can be assigned with Al 2 O 3 because aluminum is a stable element during peat accumulation, diagenetic and epigenetic processes relative to other major elements (such as Na, Mg, Si, K, Ca) [21][22][23][24][25][26][27][28][29][30], although Al is somewhat mobile in some very specific geological conditions [11,25].…”

Section: Improved Additive Log-ratio Transformationmentioning

confidence: 99%

“…The modes of occurrence of elements in coal are important because: (1) the release of toxic elements from coal are, in part, dependent on the hosts of these elements [1][2][3][4][5]; (2) they provide insights into the sources of mineral matter in coal, which result from different geological processes [1,6,7]; (3) the technologies designed for critical metals recovery from coal and coal ash largely depend on the modes of occurrence of these elements [8][9][10]; and (4) the modes of occurrence of an element can play an important role in determining the technological behavior of the element [1]. In addition to a number of physical and chemical analyses that have been used for determining the modes of occurrence in coal [11,12], some statistical methods have been commonly adopted to investigate the hosts of both major and trace elements in coal. Correlation analysis of element concentrations vs. ash yields is the simplest method that has been widely used in such studies [1].…”

Section: Introductionmentioning

confidence: 99%

Towards Consistent Interpretations of Coal Geochemistry Data on Whole-Coal versus Ash Bases through Machine Learning

Peng

et al. 2020

Minerals

View full text Add to dashboard Cite

Coal geochemistry compositional data on whole-coal basis can be converted back to ash basis based on samples' loss on ignition. However, the correlation between the concentrations of elements reported on whole-coal versus ash bases in many cases is inconsistent. Traditional statistical methods (e.g., correlation analysis) for compositional data on both bases may sometimes result in misleading results. To address this issue, we hereby propose an improved additive log-ratio data transformation method for analyzing the correlation between element concentrations reported on whole-coal versus ash bases. To verify the validity of the method proposed in this study, a data set which contains comprehensive analyses of 106 Late Paleozoic coal samples from the Datanhao mine and Adaohai Mine, Inner Mongolia, China, is used for the validity testing. A prediction model was built for performance evaluation of two methods based on the hierarchical clustering algorithm. The results show that the improved additive log-ratio is more effective in prediction for occurrence modes of elements in coal than the previously reported stability method, and therefore can be adopted for consistent interpretations of coal geochemistry compositional data on whole-coal vs. ash bases.

show abstract

Recognition of peat depositional environments in coal: A review

Cited by 299 publications

References 717 publications

Greenhouse conditions in lower Eocene coastal wetlands? – Lessons from Schöningen, Northern Germany

Greenhouse conditions in lower Eocene coastal wetlands? – Lessons from Schöningen, Northern Germany

Geological Controls on Mineralogy and Geochemistry of the Permian and Jurassic Coals in the Shanbei Coalfield, Shaanxi Province, North China

Towards Consistent Interpretations of Coal Geochemistry Data on Whole-Coal versus Ash Bases through Machine Learning

Contact Info

Product

Resources

About