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[Display omitted] • Li mostly occurs as adsorption or ion exchange in clay minerals. • The depositional environment of the No. 2 coal alternates between a hydrostatic reducing condition and an anoxic reducing environment. • Multiple geological effects influence the dissolution and migration of element-enriched minerals, resulting in the enrichment of stable components. In order to study the enrichment mechanism and depositional environment of valuable elements, this paper reports new geochemistry and mineralogy data on the No. 2 and No. 5 coals of the Nangou Mine, Ningwu Coalfield. Clay minerals and calcite are the dominant minerals in both the No. 2 and No. 5 coals. Additionally, small amounts of pyrite, quartz, rutile, and zircon are observed under scanning electron microscopy in the No. 5 coal. The studied coals in Nangou Mine are enriched in Li (CC = 8.50 and 5.80, respectively in the No. 2 and No. 5 coal), Zr (CC = 21.87 and 5.53), Nb (CC = 16.97 and 2.54), Hf (CC = 14.00 and 4.19), Ga (CC = 2.56 and 2.41). Li mostly occurs as adsorption or ion exchange in clay minerals, Nb, Zr, Hf, and Ga have organic affinities, rare earth elements and Y are mostly found in residue and organic binding states. The sediment source of the Nangou Mine is characterized by a multi-source area, with the dissolution and migration of element-enriched minerals under the influence of multiple geological effects, leading to the enrichment of valuable elements (Li, Nb, Zr, Ga, and REY). [ABSTRACT FROM AUTHOR]

Delivery riders are more vulnerable than other traffic participants, especially in vehicle-involved delivery crashes. This study aims at identifying the unobserved heterogeneities in different factors, based on 4251 vehicle-scooter-style electric bicycle (SSEB) crashes. First, some potential factors are selected from seven perspectives, and the spatiotemporal characteristics are analysed. Second, a latent class clustering method is proposed to clarify the optimal number of clusters by maximizing the heterogeneities across clusters. Third, partial proportional odds (PPO) models for the whole dataset and sub-datasets are developed to explore the heterogeneities across various clusters. Besides, marginal effects are implemented to quantify the heterogeneities. The results evidence that there are remarkable heterogeneities across different clusters, especially in riding behaviours and road conditions. Several factors only significantly affect particular clusters but not the whole dataset. The PPO models for the sub-datasets perform better in identifying the underlying heterogeneities. The results also highlight the greater roles of riding behaviours and road conditions in delivery SSEB-vehicle crashes. The top five influencing factors are running red light, using cell phones, vehicle type, reverse riding and bike lane (their maximum marginal effects exceeding +35%). The findings could support to mitigate the related crash losses.