| Citation: | DONG L,ZOU T S,XU R,et al.Nitrogen flow pattern and influencing factors of food production and consumption system in the Yangtze River Delta city cluster based on material flow model[J].Journal of Environmental Engineering Technology，2023，13（4）：1614-1624 doi: 10.12153/j.issn.1674-991X.20210732
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As an important growth pole, the release of reactive nitrogen from food production and consumption system of city clusters greatly affects regional nitrogen cycle. The material flow analysis model was introduced to quantitatively analyze the nitrogen flow patterns among cropland, livestock, aquaculture and human consumption subsystems in the Yangtze River Delta city cluster in 2019. The structure of nitrogen loss to the environment from each subsystem, the spatial distribution of nitrogen loss, and the main influencing factors of nitrogen loss intensity were also investigated. The results indicated that the total nitrogen input to the food production and consumption system was 3 472.56 Gg/a. The largest component of the total nitrogen input came from fertilizer application. The total nitrogen output from the system was 3 061.29 Gg/a, mainly represented by nitrogen loss (90.9%). The nitrogen use efficiency (NUE) of cropland, livestock, and aquaculture subsystems was 42.6%, 30.8%, and 40.1%, respectively. Moreover, nitrogen loss from cropland subsystem was the highest, which was 1 325.53 Gg/a, accounting for 47.6% of the total nitrogen loss, followed by that from human consumption subsystem, livestock subsystem and aquaculture subsystem. The spatial heterogeneity of nitrogen loss intensity among cities in the Yangtze River Delta was significant. Shanghai, Yangzhou and Yancheng rank the top three in terms of nitrogen loss intensity, which was 26.43, 23.20 and 22.26 kg/hm2, respectively, while the nitrogen loss intensity of Hangzhou, Xuancheng and Chizhou was low, being 6.14, 5.83 and 4.55 kg/hm2, respectively. The result of Pearson correlation analysis showed that the spatial heterogeneity of nitrogen loss intensity was significantly correlated with factors including economy, population, agricultural production, and land use (P<0.05 or 0.01), with the correlation coefficients ranging from 0.42 to 0.76.
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