Influence of different factors on coseismic deformation of the 2015 Mw7.8 earthquake in Nepal.

In Geophysics, topographic factors are observations that can be directly measured, but they are often ignored to simplify the model. Studying the coseismic deformation caused by earthquakes helps accurately determine the epicenter's parameterization. It provides a reference for the reasonable layout of coseismic observation stations and GNSS observation stations. After the Mw7.8 earthquake in Nepal in 2015, GCMT, USGS, GFZ, CPPT, and other institutions released their epicenter parameter. However, according to their parameters, the coseismic displacements simulated by the spectral-element method are quite different from the GNSS observations. Firstly, this paper inverts the geometric parameters of the seismogenic fault with Nepal's coseismic GNSS displacement. The spectral-element method determines the source's location and depth under the heterogeneous terrain and outputs the source parameters. Among the results of many studies, the surface source is more consistent with the generation mechanism of large earthquakes. Secondly, this paper calculates the fault slip distribution of this earthquake using SDM (Steepest Descent Method) based on GNSS and InSAR data, which is divided into 1500 subfaults, and the moment tensor of each subfault is calculated. This paper investigates the distribution characteristics of the coseismic deformation field of the 2015 Mw 7.8 earthquake in Nepal under three different models. The results show that the influence of topographic factors is ~ 20%, and the influence of heterogeneous factors is ~ 10%. This paper concludes that the influence of topographic factors is much more significant than that of heterogeneous factors, and the influence of both should be addressed in coseismic deformation calculations.

The unique association between serum 25-hydroxyvitamin D concentrations and blood lipid profiles in agriculture, forestry, and fishing occupations: Insights from NHANES 2001-2014.

BACKGROUND: The relationship of serum 25(OH)D levels and hyperlipidemia has not been explored in the Agriculture, Forestry, and Fishing (AFF) occupation. We aimed to explore the impact of serum 25(OH)D levels on lipid profiles in AFF workers, traffic drivers, and miners. METHODS: Data from 3937 adults aged 18-65 years old with completed information were obtained from the National Health and Examination Survey from 2001 to 2014. Multivariate linear regression models were used to examine the associations between serum 25(OH)D concentrations and triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and HDL-C/LDL-C ratio. Subgroup analyses for AFF workers considered age, sex, BMI, work activity, months worked, and alcohol consumption. Non-linear relationships were explored using curve fitting. RESULTS: Serum 25(OH)D levels differed between groups (AFF: 60.0 ± 21.3 nmol/L, drivers: 56.6 ± 22.2 nmol/L, miners: 62.8 ± 22.3 nmol/L). Subgroup analysis of the AFF group showed that participants with serum 25(OH)D ≥50 nmol/L, females, and BMI <30 kg/m2 demonstrated improved HDL-C levels correlating with higher serum 25(OH)D. Serum 25(OH)D in AFF workers had a reversed U-shaped relationship with TG and TC, and a U-shaped relationship with HDL-C, with HDL-C, with inflection points at 49.5 nmol/L for TG and TC, and 32.6 nmol/L for HDL-C. CONCLUSIONS: Serum 25(OH)D levels are associated with lipid profiles, and the relationship varies among occupational groups. AFF workers, facing unique occupational challenges, may benefit from maintaining adequate serum 25(OH)D levels to mitigate adverse lipid profiles and reduce cardiovascular risk.

Effects of seasonal changes on the carbon dynamics in mixed coniferous forests.

We investigated the residual rate and mass loss rate of litter, as well as the carbon release dynamics of litter and soil across seasons, to better understand the effects of seasonal fluctuations on carbon dynamics in mixed coniferous forests. The study was carried out in natural mixed coniferous forests in the Xiaoxinganling region of Heilongjiang Province, China, and the number of temperature cycles in the unfrozen season, freeze-thaw season, frozen season, and thaw season was controlled. The goal of the study was to examine how the carbon release dynamics of litter and soil respond to the freeze-thaw process and whether there are differences in carbon release dynamics under different seasons. Repeated-measures analysis of variance was used to analyze the residual mass rate and mass loss rate of litter, litter organic carbon and soil organic carbon during the unfrozen season, freeze-thaw season, frozen season, and thaw season. Litter decomposition was highest in the unfrozen season (15.9%~20.3%), and litter and soil carbon were sequestered throughout this process. Temperature swings above and below 0°C during the freeze-thaw season cause the litter to physically fragment and hasten its decomposition. Decomposition of litter was still feasible during the frozen season, and it was at its lowest during the thaw season (7.2%~7.8%), when its organic carbon was transported to the soil. Carbon migrates from undecomposed litter to semi-decomposed litter and then to soil. The carbon in the environment is fixed in the litter (11.3%~18.2%) and soil (34.4%~36.7%) in the unfrozen season, the carbon-fixing ability of the undecomposed litter in the freeze-thaw season is better, and the carbon in the semi-decomposed litter is mostly transferred to the soil; the carbon-fixing ability of the litter in the frozen season is worse (-3.9%~ -4.3%), and the organic carbon in the litter is gradually transferred to the soil. The carbon-fixing ability of the undecomposed litter in the thaw season is stronger, and the organic carbon in the semi-decomposed litter is mostly transferred to the soil. Both litter and soil can store carbon; however, from the unfrozen season until the thaw season, carbon is transported from undecomposed litter to semi-decomposed litter and to the soil over time.

Conditional inference trees in the assessment of tree mortality rates in the transitional mixed forests of Atlantic Canada.

Long-term predictions of forest dynamics, including forecasts of tree growth and mortality, are central to sustainable forest-management planning. Although often difficult to evaluate, tree mortality rates under different abiotic and biotic conditions are vital in defining the long-term dynamics of forest ecosystems. In this study, we have modeled tree mortality rates using conditional inference trees (CTREE) and multi-year permanent sample plot data sourced from an inventory with coverage of New Brunswick (NB), Canada. The final CTREE mortality model was based on four tree- and three stand-level terms together with two climatic terms. The correlation coefficient (R2) between observed and predicted mortality rates was 0.67. High cumulative annual growing degree-days (GDD) was found to lead to increased mortality in 18 tree species, including Betula papyrifera, Picea mariana, Acer saccharum, and Larix laricina. In another ten species, including Abies balsamea, Tsuga canadensis, Fraxinus americana, and Fagus grandifolia, mortality rates tended to be higher in areas with high incident solar radiation. High amounts of precipitation in NB's humid maritime climate were also found to contribute to heightened tree mortality. The relationship between high GDD, solar radiation, and high mortality rates was particularly strong when precipitation was also low. This would suggest that although excessive soil water can contribute to heightened tree mortality by reducing the supply of air to the roots, occasional drought in NB can also contribute to increased mortality events. These results would have significant implications when considered alongside regional climate projections which generally entail both components of warming and increased precipitation.