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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.
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Details
; Liu, Wenfeng 2
; Yang, Hong 3 ; La Zhuo 4
; Tong, Yindong 5 ; Liu, Yilin 6 ; Yang, Yonghui 7 ; Zhou, Lingfeng 8 1 School of Geographic Sciences, Hebei Normal University, Shijiazhuang, 050024, People’s Republic of China; Hebei Key Laboratory of Environmental Change and Ecological Construction, Shijiazhuang 050024, People’s Republic of China; Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang 050024, People’s Republic of China
2 Center for Agricultural Water Research in China, College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, People’s Republic of China
3 Eawag, Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstrasse 133, CH-8600 Duebendorf, Switzerland; Department of Environmental Sciences, MGU, University of Basel, Petersplatz 1, CH-4003 Basel, Switzerland
4 Northwest Agriculture and Forestry University, Yangling 712100, People’s Republic of China; Institute of Soil and Water Conservation, Chinese Academy of Sciences, Yangling 712100, People’s Republic of China
5 School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, People’s Republic of China
6 Northwest Agriculture and Forestry University, Yangling 712100, People’s Republic of China
7 Key Laboratory of Agricultural Water Resources, Hebei Laboratory of Agricultural Water-saving, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 286 Huaizhong Road, Shijiazhuang 050021, People’s Republic of China; University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
8 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, People’s Republic of China




