Nitrogen flow patterns in the food system among cities within urban agglomeration: A case study of the Pearl River Delta region

Human activities along the entire food supply-consumption-waste treatment-recycling chain have an essential influence on Nitrogen (N) metabolic features, especially for densely-populated urban agglomeration. A few studies carried out research on detailed analysis and comparison of N flow patterns along the entire food chain among cities, to recognize these influences and accordingly explore effective measures for improving N use efficiencies. In this study, we developed an integrated N flow analysis model to quantify N flows in the food system illustrated by production, processing, consumption, and waste management sectors. Influence of anthropogenic activities on N flow patterns is recognized through comparison among cities and predictions of future scenarios. Using the Pearl River Delta (PRD) region as a case study, we find that (1) in 2016, the annual N import into the production sector in the food system in the PRD region was about 714.5 Gg, among which only 241.6 Gg entered food products. The removal rate of N pollution in all waste stream was about 62.3%, and only 9% of N became resources through reclamation. (2) Among the nine cities in the PRD region, the average amounts of N pollution emission to the air, water, and soil all range from 0.57-5.38 kg cap(-1) yr(-1), showing significant discrepancy among cities. Cities with relatively lower economic development undertake substantial N pollution embedded in their exported agricultural products. (3) Recycling of agricultural waste is the prior N management measure for Zhaoqing, Jiangmen, and Huizhou, while highly urbanized cities should mainly concentrate on recycling of food waste and sewage sludge. We further put forward suggestions such as cross-city resource recycling to realize better N resource recycling and pollution reduction on the whole urban agglomeration scale. This study provides an in-depth example of depicting N flow patterns and identifying proper N management measures for urban agglomerations. (c) 2019 Elsevier B.V. All rights reserved.