Data integration analysis: Heavy metal pollution in China's large-scale cattle rearing and reduction potential in manure utilization
文献类型:期刊论文
作者 | Xu, Yan1,2; Li, Jing1; Zhang, Xubo1; Wang, Lingqing3; Xu, Xiangbo1; Xu, Li4; Gong, Huarui1,2; Xie, Hanyou1,2; Li, Fadong1 |
刊名 | JOURNAL OF CLEANER PRODUCTION
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出版日期 | 2019-09-20 |
卷号 | 232页码:308-317 |
关键词 | Large-scale farming Feed-feces-soil chain Heavy metal pollution Livestock manure Passivating agent |
ISSN号 | 0959-6526 |
DOI | 10.1016/j.jclepro.2019.05.337 |
通讯作者 | Li, Jing(jingli@igsnrr.ac.cn) ; Xu, Li(xuliforever@163.com) |
英文摘要 | Manure generated from livestock farming is currently the focus of attention of global scientific community. China's livestock manure is extremely rich with a high potential for heavy metal contamination. Here, data were collected for 2000-2018.6 from China's National Statistical Data, Elsevier, China National Knowledge Infrastructure and other literature databases and analyzed for "feed-feces-soil" chain of heavy metals. An integrated analysis was done to explore the reductive effect of existing technologies on heavy metals in cattle manure. Excessive addition of heavy metal elements to feed is widespread in China. High amounts of undigested heavy metals are excreted through animal manure. The study showed that spatial variability of heavy metal contents in cattle manure was extremely large. The excess cupper (Cu) was higher than that of zinc (Zn), and was higher in southern than in northern China. Chemical treatments were more effective than physical and biological ones in controlling heavy metal pollution. The optimal reduction ratios of the effective Zn and Cu contents were 21.72% and 33.11%, respectively. The Zn and Cu contents could be reduced by 1.96 x 10(7) and 5.92 x 10(6) kg per year, respectively. This could reduce the risks posed by heavy metals after manure-based fertilizer applications in crop cultivation. (C) 2019 Published by Elsevier Ltd. |
WOS关键词 | IN-SITU REMEDIATION ; LIVESTOCK ; SOIL ; SPECIATION ; BIOCHAR ; BIOAVAILABILITY ; BIOMASS ; COPPER ; FIELD ; POWER |
资助项目 | Strategic Priority Research Program of Chinese Academy of Sciences[XDA23050101] ; National Key Research and Development Program of China[2016YFD0300808] ; Youth Fund of Beijing Academy of Agriculture and Forestry Sciences[QNJJ201717] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2017073] |
WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
语种 | 英语 |
WOS记录号 | WOS:000477784000028 |
出版者 | ELSEVIER SCI LTD |
资助机构 | Strategic Priority Research Program of Chinese Academy of Sciences ; National Key Research and Development Program of China ; Youth Fund of Beijing Academy of Agriculture and Forestry Sciences ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/58219] ![]() |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Li, Jing; Xu, Li |
作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 4.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Agr Stand & Testing, Beijing 100097, Peoples R China |
推荐引用方式 GB/T 7714 | Xu, Yan,Li, Jing,Zhang, Xubo,et al. Data integration analysis: Heavy metal pollution in China's large-scale cattle rearing and reduction potential in manure utilization[J]. JOURNAL OF CLEANER PRODUCTION,2019,232:308-317. |
APA | Xu, Yan.,Li, Jing.,Zhang, Xubo.,Wang, Lingqing.,Xu, Xiangbo.,...&Li, Fadong.(2019).Data integration analysis: Heavy metal pollution in China's large-scale cattle rearing and reduction potential in manure utilization.JOURNAL OF CLEANER PRODUCTION,232,308-317. |
MLA | Xu, Yan,et al."Data integration analysis: Heavy metal pollution in China's large-scale cattle rearing and reduction potential in manure utilization".JOURNAL OF CLEANER PRODUCTION 232(2019):308-317. |
入库方式: OAI收割
来源:地理科学与资源研究所
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