Discriminative algorithm approach to forecast Cd threshold exceedance probability for rice grain based on soil characteristics

doi:10.1016/j.envpol.2020.114211

	Discriminative algorithm approach to forecast Cd threshold exceedance probability for rice grain based on soil characteristics
	Yang, Jun 1,2; Zhao, Chen 1,2; Yang, Junxing 1,2; Wang, Jingyun 1,2; Li, Zhitao 3; Wan, Xiaoming 1,2; Guo, Guanghui 1,2; Lei, Mei 1,2; Chen, Tongbin 1,2
刊名	ENVIRONMENTAL POLLUTION
	2020-06-01
卷号	261 页码:12
关键词	Probabilistic forecasting model Discriminative algorithm Cadmium Rice grain Soil
ISSN号	0269-7491
DOI	10.1016/j.envpol.2020.114211
通讯作者	Zhao, Chen(zhaoc.14b@igsnrr.ac.cn)
英文摘要	The relationship between cadmium (Cd) concentration in rice grains and the soil that they are cultivated in is highly uncertain due to the influence of soil properties, rice varieties, and other undetermined factors. In this study, we introduce the probability of exceeding the threshold to characterize this uncertainty and then, build a probabilistic forewarning model. Additionally, a number of associated factors have been used as parameters to improve model performance. Considering that the physicochemical properties and Cd concentration in the soil (Cd-soil) do not follow a normal distribution, and are not independent of each other, a discriminative algorithm, represented by a logistic regression (LR), performed better than generative algorithms, such as the naive Bayes and quadratic discriminant analysis models. The performance of the LR based model was found to be 0.5% better in the case of the univariate model (Cd-soil) and 4.1% better with a multivariate model (soil properties used as additional factors) (p < 0.01). The output of the LR based model predicted probabilities that were positively correlated to the true exceedance rate (R-2 = 0.949,p < 0.01), within an exceedance threshold range of 0.1-0.4 mg kg(-1) and a mean deviation of 5.75%. A sensitivity analysis showed that the effect of soil properties on the exceedance probability weakens with an increase in Cd concentration in rice grains. When the threshold is below 0.15 mg kg(-1), soil pH strongly influences the exceedance probability. As the threshold increases, the influence of pH on the exceedance probability is gradually superseded. By quantifying the uncertainty regarding the relationship between Cd concentration in rice grains and soil, the discriminative algorithm-based probabilistic forecasting model offers a new way to assess Cd pollution in rice grown in contaminated paddy fields. (C) 2020 Elsevier Ltd. All rights reserved.
资助项目	Natural Science Foundation of China[41771510] ; National Key Research and Development Program of China[2018YFD0800601]
WOS关键词	HEAVY-METAL CONCENTRATIONS ; LOGISTIC-REGRESSION ; ORGANIC-MATTER ; CADMIUM UPTAKE ; PADDY FIELDS ; MICROBIAL BIOMASS ; TRACE-ELEMENTS ; EDIBLE PARTS ; MINING AREA ; HAN RIVER
WOS研究方向	Environmental Sciences & Ecology
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000531106500057
资助机构	Natural Science Foundation of China ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/159756]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhao, Chen
作者单位	1.Chinese Acad Sci, Ctr Environm Remediat, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Minist Ecol & Environm, Tech Ctr Soil Agr & Rural Ecol & Environm, Beijing 100012, Peoples R China
推荐引用方式 GB/T 7714	Yang, Jun,Zhao, Chen,Yang, Junxing,et al. Discriminative algorithm approach to forecast Cd threshold exceedance probability for rice grain based on soil characteristics[J]. ENVIRONMENTAL POLLUTION,2020,261:12.
APA	Yang, Jun.,Zhao, Chen.,Yang, Junxing.,Wang, Jingyun.,Li, Zhitao.,...&Chen, Tongbin.(2020).Discriminative algorithm approach to forecast Cd threshold exceedance probability for rice grain based on soil characteristics.ENVIRONMENTAL POLLUTION,261,12.
MLA	Yang, Jun,et al."Discriminative algorithm approach to forecast Cd threshold exceedance probability for rice grain based on soil characteristics".ENVIRONMENTAL POLLUTION 261(2020):12.