Evaluating agricultural grey water footprint with modeled nitrogen emission
Yuanchao Hu, Yunfeng Huang, Jianxiong Tang, Bing Gao, Miaohong Yang,
Fanxin Meng,Shenghui Cui*
Evaluating agricultural grey water footprint with modeled nitrogen emission data
刊物:Resources, Conservation and Recycling
Food production is one of the major water pollution sources, due to the consistently intensive nutrient loss it generates. Grey water footprint (GWF) is commonly used as the indicator to assess environmental performance of human activities and water quality management. Current quantification of GWF mainly depends on existing parameters, sparse environmental census and monitoring, and hydrological models, which may lead to an inefficient evaluation of water pollution. Here, we apply a more applicable and flexible methodology based on modelled nitrogen emission inventories and water quality standards, to evaluate the GWF of food production with detailed food types and production process. We found that reactive nitrogen dominates the hydrological pollution in food production at the national level, and hence we quantified the emissions with details of processes, food types and Chinese regions. The GWF intensities (GWF of per kg food) of vegetable food products from this research were generally 3–70 times larger than those from key previous studies, while the animal food products showed even larger differences. However, our reasonable and comparable reactive nitrogen results bring additional confidence to the GWF results. As the quantification of reactive nitrogen emissions can easily fit into the targeted temporal and spatial range, the example introduced in this research can help to recognize the key food type and production process.
Key words:Food production;Agriculture;Grey water footprint;Nitrogen;China
推荐引用格式:Yuanchao Hu, Yunfeng Huang, Jianxiong Tang, BingGao, Miaohong Yang, FanxinMeng, Shenghui Cui*.Evaluating agricultural grey water footprint with modeled nitrogen emission data. Resources, Conservation & Recycling. 2018, 138: 64–73.