Leaching and migration characteristics of nitrogen during coastal saline soil remediation by combining humic acid with gypsum and bentonite

Coastal saline soil is an important resource that is widely-distributed and available for mitigation, development and utilization. Adding exogenous materials to improve saline soil has been shown to be an effective measure to increase its agricultural productivity. However, there is limited information about the risk of nitrogen (N) leaching during coastal saline soil improvement process. Therefore, the leaching and migration characteristics of N in saline soils of the Yellow River Delta (Shandong Province, China) with the addition of weathered coal humic acid (HA), gypsum and bentonite, were examined using soil columns and 15N isotope tracers. The results showed that co-application of HA and gypsum could significantly increase the soil penetration rate (SPR) and reduce the pH of saline soil but did not increase N leaching, specifically an application ratio of 1:3 (HA: gypsum) based on weight. The co-application of HA and bentonite could not increase the SPR and reduce the pH, but could inhibit nitrification and better conserve 15N of saline soil. Therefore, we believe that bentonite cannot replace gypsum but could be used as an auxiliary agent to achieve better N retention. The mixed addition of HA and gypsum can achieve the dual effect of improving saline soil and maintaining N, which is beneficial to environmental protection and high-quality development of agricultural production in the Yellow River Delta

An adaptive spot placement method on Cartesian grid for pencil beam scanning proton therapy

Pencil beam scanning proton radiotherapy (RT) offers flexible proton spot placement near treatment targets for delivering tumoricidal radiation dose to tumor targets while sparing organs-at-risk. Currently the spot placement is mostly based on a non-adaptive sampling (NS) strategy on a Cartesian grid. However, the spot density or spacing during NS is a constant for the Cartesian grid that is independent of the geometry of tumor targets, and thus can be suboptimal in terms of plan quality (e.g. target dose conformality) and delivery efficiency (e.g. number of spots). This work develops an adaptive sampling (AS) spot placement method on the Cartesian grid that fully accounts for the geometry of tumor targets. Compared with NS, AS places (1) a relatively fine grid of spots at the boundary of tumor targets to account for the geometry of tumor targets and treatment uncertainties (setup and range uncertainty) for improving dose conformality, and (2) a relatively coarse grid of spots in the interior of tumor targets to reduce the number of spots for improving delivery efficiency and robustness to the minimum-minitor-unit (MMU) constraint. The results demonstrate that (1) AS achieved comparable plan quality with NS for regular MMU and substantially improved plan quality from NS for large MMU, using merely about 10% of spots from NS, where AS was derived from the same Cartesian grid as NS; (2) on the other hand, with similar number of spots, AS had better plan quality than NS consistently for regular and large MMU