Virtual nitrogen and phosphorus flow associated with interprovincial crop trade and its effect on grey water stress in China
Virtual nitrogen and phosphorus flow associated with interprovincial crop trade and its effect on grey water stress in China
复制标题
中国省际农作物贸易虚拟氮磷流量及其对灰水胁迫的影响
DOI:
10.1088/1748-9326/ac3604
复制
发表时间:
2021-11
影响因子:
6.7
通讯作者:
Lingfeng Zhou
中科院分区:
文献类型:
--
作者:
D;an Ren;Wenfeng Liu;Hong Yang;La Zhuo;Yindong Tong;Yilin Liu;Yonghui Yang;Lingfeng Zhou
Abstract. The grey water footprint (GWF) is defined as freshwater requirements for diluting pollutants in receiving water bodies. It is widely used to measure the impact of pollutant loads on water resources. GWF can be transferred from one area to another through trade. Although pollution flow has previously been investigated at the national level, there has been no explicit study on the extent to which crop trade affects GWF across regions and the associated changes in grey water stress (GWS). This study analyzes pollution flow associated with interprovincial crop trade based on nitrogen (N) and phosphorus (P) loss intensity of three major crops, namely, maize, rice and wheat, which is simulated by a grid-based crop model for the period 2008–2012, and evaluates the spatial patterns of GWS across China. The results indicate that the integrated national GWF for N and P was 1271 billion m3 yr−1, with maize, rice, and wheat contributing 39%, 37%, and 24%, respectively. Through interprovincial crop trade, southern China outsourced substantial N and P losses to the north, leading to a 30% GWS increase in northern China and 66% GWS mitigation in southern China. Specifically, Jilin, Henan, and Heilongjiang Provinces in the northern China showed increases in GWS by 161%, 114%, and 55%, respectively, while Fujian, Shanghai, and Zhejiang in the south had GWS reductions of 83%, 85%, and 80%, respectively. It was found that the interprovincial crop trade led to reduced national GWF and GWS. Insights into GWF and GWS can form the basis for policy developments on N and P pollution mitigation across regions in China.
登录
查看更多内容
影响因子:
5.1
作者:
J. Franke;C. Müller;J. Elliott;A. Ruane;J. Jägermeyr;J. Balkovič;P. Ciais;Marie Dury;P. Falloon-P.
通讯作者:
J. Franke;C. Müller;J. Elliott;A. Ruane;J. Jägermeyr;J. Balkovič;P. Ciais;Marie Dury;P. Falloon-P.
影响因子:
5.4
作者:
Hong Yang;A. Zehnder
通讯作者:
Hong Yang;A. Zehnder
DOI:
10.1073/pnas.1404749111
发表时间:
2014-07-08
影响因子:
11.1
作者:
Dalin, Carole;Hanasaki, Naota;Rodriguez-Iturbe, Ignacio
通讯作者:
Rodriguez-Iturbe, Ignacio
影响因子:
11.1
作者:
Bo Wu;W. Zeng;Honghan Chen;Yue Zhao
通讯作者:
Bo Wu;W. Zeng;Honghan Chen;Yue Zhao
DOI:
10.1073/pnas.1718153115
发表时间:
2018-05
期刊:
Proceedings of the National Academy of Sciences
影响因子:
--
作者:
Jing Sun;H. Mooney;Wenbin Wu;Huajun Tang;Yuxin Tong;Zhenci Xu;Baorong Huang;Yeqing Cheng;
通讯作者:
Jing Sun;H. Mooney;Wenbin Wu;Huajun Tang;Yuxin Tong;Zhenci Xu;Baorong Huang;Yeqing Cheng;