Most biosphere and contamination assessment models are based on uniform soil conditions, since single coefficients are used to describe the transfer of contaminants to the plant. Indeed, physical and chemical characteristics and root distribution are highly variable in the soil profile. These parameters have to be considered in the formulation of a more realistic soil-plant transfer model for naturally structured soils. The impact of monolith soil structure (repacked and structured) on Zn and Mn uptake by wheat was studied in a controlled tracer application (dye and radioactive) experiment. We used Brilliant Blue and Sulforhodamine B to dye flow lines and 65Zn and 54Mn to trace soil distribution and plant uptake of surface-applied particle-reactive contaminants. Spatial variation of the soil water content during irrigation and plant growth informs indirectly about tracer and root location in the soil profile. In the structured monolith, a till pan at a depth of 30 cm limited vertical water flow and root penetration into deeper soil layers and restricted tracers to the upper third of the monolith. In the repacked monolith, roots were observed at all depths and fingering flow allowed for the fast appearance of all tracers in the outflow. These differences between the two monoliths are reflected by significantly higher 54Mn and 65Zn uptake in wheat grown on the structured monolith. The higher uptake of Mn can be modelled on the basis of radionuclide and root distribution as a function of depth and using a combination of preferential flow and rooting. The considerably higher uptake of Zn requires transfer factors which account for variable biochemical uptake as a function of location.
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http://dx.doi.org/10.1016/s0265-931x(01)00082-0 | DOI Listing |
Front Pharmacol
January 2025
School Hospital, Guizhou Medical University, Guiyang, China.
Thunb. (. ) is a shrub or tree of the genus , family Lamiaceae, which is widely distributed in China, Korea, India, Japan and Philippines.
View Article and Find Full Text PDFSci Rep
January 2025
Geology and Sustainable Mining Institute (GSMI), Mohammed VI Polytechnic University, Ben Guerir, Morocco.
Accurate statistical modeling of wind speed variability is crucial for assessing wind energy potential, particularly in regions with low wind speeds and significant calm hours. This study evaluates the Champernowne distribution as a novel model for wind speed analysis, comparing its performance with the two-parameter Weibull, three-parameter Weibull, and Rayleigh-Rice distributions. Wind speed data at 10 m hub height over three years (2021-2023) from Ben Guerir, Morocco, were analyzed using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Coefficient of Determination (R2), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC).
View Article and Find Full Text PDFPhysiol Plant
January 2025
State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-Di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
Pinellia ternata is an herb species in the Pinellia genus with significant economic value due to its medicinal properties. Understanding the accumulation and spatial distribution characteristics of metabolites during the development of the medicinal part, the rhizome of P. ternata (PR), provides a basis for targeted metabolic regulation and quality evaluation.
View Article and Find Full Text PDFSoil salinization poses a significant ecological and environmental challenge both in China and across the globe. Plant growth-promoting rhizobacteria (PGPR) enhance plants' resilience against biotic and abiotic stresses, thereby playing a vital role in soil improvement and vegetation restoration efforts. PGPR assist plants in thriving under salt stress by modifying plant physiology, enhancing nutrient absorption, and synthesizing plant hormones.
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Radiation Oncology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Objectives: Implementing pre-treatment patient-specific quality assurance (prePSQA) for cancer patients is a necessary but time-consuming task, imposing a significant workload on medical physicists. Currently, the prediction methods used for prePSQA fall under the category of supervised learning, limiting their generalization ability and resulting in poor performance on new data. In the context of this work, the limitation of traditional supervised models was broken by proposing a conditional generation method utilizing unsupervised diffusion model.
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