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基于生物可利用砷和土壤特性的稻米砷积累预测模型。

A predictive model for arsenic accumulation in rice grains based on bioavailable arsenic and soil characteristics.

机构信息

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China.

Jiaxing Academy of Agricultural Sciences, Xiuzhou District, Jiaxing 314016, China.

出版信息

J Hazard Mater. 2021 Jun 15;412:125131. doi: 10.1016/j.jhazmat.2021.125131. Epub 2021 Jan 14.

DOI:10.1016/j.jhazmat.2021.125131
PMID:33516100
Abstract

Arsenic (As) is a well-known human carcinogen, and rice consumption is the main way Chinese people are exposed to As. In this study, 14 kinds of paddy soils were collected from the main rice-producing areas in China. The results showed that rice roots and leaves accumulated more As than stems and grains in the following sequence: As> As> As> As. The accumulation of As by rice grains mainly depends on the total As and bioavailable As (0.43 mol/L HNO extractable As), which explained 32.2% and 22.2% of the variation in the grain As, respectively. In addition, soil pH, organic matter (OM) and clay contents were the major factors affecting grain As, explaining 13.1%, 7.9% and 5.3% of the variation, respectively. An effective prediction model was established via multiple linear regression as As= 0.024 BAs - 0.225 pH+ 0.013 OM+ 0.648 EC - 0.320 TN - 0.088 TP - 0.002 AS+ 2.157 (R =0.68, P < 0.01). Through the verification of the samples from both pot experiments and paddy fields, the model successfully provided accurate predictions for rice grain As.

摘要

砷(As)是一种众所周知的人类致癌物,而食用大米是中国人接触砷的主要途径。本研究采集了中国主要水稻产区的 14 种稻田土壤。结果表明,水稻根系和叶片对 As 的积累量大于茎和籽粒,其积累顺序为:As>As>As>As。水稻籽粒中 As 的积累主要取决于总 As 和生物可利用态 As(0.43 mol/L HNO3 可提取态 As),分别解释了籽粒 As 变化的 32.2%和 22.2%。此外,土壤 pH 值、有机质(OM)和粘粒含量是影响籽粒 As 的主要因素,分别解释了 13.1%、7.9%和 5.3%的变化。通过多元线性回归建立了一个有效的预测模型,As=0.024 BAs-0.225 pH+0.013 OM+0.648 EC-0.320 TN-0.088 TP-0.002 AS+2.157(R=0.68,P<0.01)。通过盆栽实验和田间试验样本的验证,该模型成功地对水稻籽粒 As 进行了准确预测。

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