Literature DB >> 34365211

Remediation of arsenic-contaminated paddy soil: Effects of elemental sulfur and gypsum fertilizer application.

Dengxiao Zhang1, Guanghui Du1, Wenjing Zhang1, Ya Gao1, Hongbin Jie1, Wei Rao1, Ying Jiang1, Daichang Wang2.   

Abstract

Heavy metal(loid) contamination represents an immense challenge in sustainable agriculture. Arsenic, in particular, poses a great risk to the quality of agricultural products (e.g., rice grain). The sulfur amendment is recommended as an effective practice to remediate heavy metal(loid)-polluted soil, given its function in enhancing crop production and alleviating heavy metal(loid) accumulation in the plant. This study aims to investigate the roles of sulfur fertilizer on arsenic accumulation in rice and to explore the key mechanisms. In this study, Elemental sulfur (ES) and gypsum sulfur (GS) were chosen as sulfur fertilizers, with different application rates (0, 0.15, and 0.30 g S kg-1 soil). The results showed that ES and GS treatment significantly increased rice grain yield by 46.6-59.7% and significantly reduced the rice grain arsenic content by more than 39.1%. The sulfur treatment decreased soil pe + pH values. ES treatment increased the availability of arsenic in the bulk soil, whereas GS showed little effect. Sulfur application promoted the formation of iron and manganese plaques, which could suppress the migration of arsenic from soil to rice root. In addition, the sulfur treatment decreased the arsenic that migrating from rice roots to grains by 33.3-66.7%. This study indicates that sulfur application could increase arsenic availability in paddy soil; however, it can inhibit arsenic accumulation in rice grains via increasing the root plaques content and inhibiting the translocation of arsenic from roots to grains.
Copyright © 2021. Published by Elsevier Inc.

Entities:  

Keywords:  Fe/Mn plaque; Rhizosphere; Rice arsenic accumulation; Soil arsenic fraction; Sulfur fertilizer

Year:  2021        PMID: 34365211     DOI: 10.1016/j.ecoenv.2021.112606

Source DB:  PubMed          Journal:  Ecotoxicol Environ Saf        ISSN: 0147-6513            Impact factor:   6.291


  1 in total

1.  A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products.

Authors:  Zuzheng Wang; Zhixiang Wu; Minke Zou; Xin Wen; Zheng Wang; Yuanzhang Li; Qingchuan Zhang
Journal:  Foods       Date:  2022-03-13
  1 in total

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