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A network flow analysis of nitrogen metabolism in Beijing, China Yan Zhang, Hanjing Lu, Brian Fath, Hongmei Zheng, Xiaoxi Sun, and Yanxian Li Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00181 • Publication Date (Web): 12 Jul 2016 Downloaded from http://pubs.acs.org on July 13, 2016

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Full title: A network flow analysis of the nitrogen metabolism in Beijing, China

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Authors: Yan Zhang a,*, Hanjing Lu a, Brian D. Fath b, c, Hongmei Zhenga, Xiaoxi Sun a,

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Yanxian Li a

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Author family names: Zhang, Lu, Fath, Zheng, Sun and Li

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Affiliation:

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a

State Key Joint Laboratory of Environment Simulation and Pollution Control, School

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of Environment, Beijing Normal University, Xinjiekouwai Street No. 19, Beijing

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100875, China

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b

Department of Biological Sciences, Towson University, Towson, MD 21252, USA

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Dynamic Systems, International Institute for Applied Systems Analysis, Laxenburg,

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Austria

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* Corresponding author: ZHANG Yan

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Postal address: State Key Joint Laboratory of Environmental Simulation and Pollution

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Control, School of Environment, Beijing Normal University, Xinjiekouwai Street No.

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19, Beijing 100875, China

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Tel./fax: +86 10-5880-7280.

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E-mail address: [email protected]


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Abstract

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Rapid urbanization results in high nitrogen flows and subsequent environmental

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consequences. In this study, we identified the main metabolic components (nitrogen

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inputs, flows, and outputs) and used ecological network analysis to track the direct and

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integral (direct + indirect) metabolic flows of nitrogen in Beijing, China, from 1996 to

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2012 and to quantify the structure of Beijing’s nitrogen metabolic processes. We found

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that Beijing’s input of new reactive nitrogen (Q, which represents nitrogen obtained

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from the atmosphere or nitrogen-containing materials used in production and

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consumption to support human activities) increased from 431 Gg in 1996 to 507 Gg in

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2012. Flows to the Industry, Atmosphere, and Household, and components of the

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system were clearly largest, with total integrated inputs plus outputs from these nodes

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accounting for 31, 29, and 15%, respectively, of the total integral flows for all paths.

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The flows through the Sewage Treatment and Transportation components showed

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marked growth, with total integrated inputs plus outputs increasing to 3.7 and 5.2 times

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their 1996 values, respectively. Our results can help policymakers to locate the key

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nodes and pathways in an urban nitrogen metabolic system so they can monitor and

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manage these components of the system.

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Keywords: Urban ecology; Nitrogen metabolism; Ecological network analysis; Direct

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flows; Integral flows; Beijing

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1. Introduction

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Nitrogen is an essential element in socio-ecological systems and its flows can be

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traced through its biogeochemical cycle and human systems by means of material-flow

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analysis. The vast majority of global nitrogen (N) exists as stable atmospheric N2 and



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thus, is unavailable to organisms until it is converted into reactive N, which can be

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taken up by microbes and plants. (“Reactive N” includes inorganic reduced forms of N,

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inorganic oxidized forms, and organic compounds.) In a natural system, biological

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fixation of nitrogen is the main pathway for creating reactive N, and the processes of

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nitrogen fixation and denitrification tend to be in dynamic balance.1 In many terrestrial

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ecosystems, primary production and other ecosystem processes are constrained by low

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rates of nutrient supply.2 The availability of reactive nitrogen has therefore been

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identified as a limiting factor for plant growth in both agricultural and natural

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ecosystems. In this context, the advent of the Haber-Bosch process greatly

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supplemented natural nitrogen fixation through industrial nitrogen fixation1. In addition,

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humans also alter the global N cycle via combustion of fossil fuels, production of

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nitrogen fertilizers, cultivation of nitrogen-fixing legumes, and other actions.2, 3 These

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interventions have substantially increased plant productivity and biomass accumulation,

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at least in the short-term.4 However, excessive N can induce a series of economic and

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environmental problems, including contributing to the greenhouse effect, destruction of

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the ozone layer, creation of acid rain and nitrate pollution of groundwater,

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eutrophication of lakes and offshore water, and local, national, and global biodiversity

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reduction.2, 5

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Beijing, as China's capital, consumes huge quantities of nitrogenous materials. The

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city’s complex nitrogen cycle represents both natural processes and the influence of

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human activity, and therefore provides a good case study for nitrogen flows. Between

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1996 and 2012, the consumption of agricultural products in Beijing’s food sector nearly

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doubled, and the consumption of meat products tripled.6 During the same period, energy

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consumption increased to 1.5 times its 1996 value.7 These changes have ultimately


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resulted in increased nitrogen flows through and losses from the system. From 1996

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through 2012, the amount of household sewage discharge increased from 0.9 Mt to 1.5

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Mt annually,8 and the contents of nitrogen and other nutrients in the sewage resulted in

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water eutrophication because of inadequate treatment capacity. In addition, the emission

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of nitrogen oxides (NOx), a key group of pollutants that cause air quality problems, has

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increased. According to the Ministry of Environmental Protection,9 Beijing’s air quality

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met national safety standards on only 48% of the days in 2013, and 16.2% of the

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remaining days showed high levels of pollution. In this context, the current study

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explores the characteristics of Beijing’s nitrogen metabolism, with particular emphasis

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on examining the key natural and anthropogenic metabolic processes in this

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human-dominated socio-ecological system from the perspective of urban metabolic

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theory. The results of these investigations will provide a scientific basis for improving

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the efficiency of urban nitrogen use.

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The basic tool required in such metabolic research is an accounting framework.

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Jaworski et al.10 were the first to use material-flow analysis to estimate the nitrogen and

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phosphorus balances and loads, in their case study of the Potomac River Basin. Similar

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approaches have been used to study other basins and identify the reasons for increased

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estuarine nitrogen outputs since the invention of Haber-Bosch nitrogen fixation. The

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areas studied included the North Atlantic Ocean11 and the Waquoit Bay Land Margin

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Ecosystem12. In the accounting framework for river basins, some researchers have

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emphasized the nitrogen sources affecting the rivers,13, 14 whereas others have examined

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the efficiency of nitrogen use15-17 and the fate of nitrogen18, 19. For anthropic input of

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reactive N at a catchment scale, researchers considered only agricultural activities, since

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the nitrogen load in the water was their main concern.



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Based on lessons learned from previous accounting analyses of nitrogen balances in

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river basins, several researchers extended the approach from the scale of a river basin to

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urban20 and national scales. 21, 22 Deng et al.23 studied the nitrogen sources and sinks in

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the Yangtze River Delta economic region, but focused only on nitrogen fixation

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associated with fertilizer use and agriculture. Gu et al.24, 25 studied the nitrogen cycles of

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two Chinese cities: Shanghai and Hangzhou. In recent years, the temporal and spatial

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variation of nitrogen inputs at a national scale have become a research focus in both

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China26-28 and the United States.29 To account for anthropic inputs of reactive N at urban

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and national scales, researchers have added accounting terms such as nitrogen

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consumed and emitted by pets, fossil fuel combustion, flows of nitrogenous chemical

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materials, and sewage treatment systems. They have also added more socioeconomic

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components to their models to account for the greater complexity of urban and national

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nitrogen cycles.

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Many researchers have focused on the total nitrogen or reactive N inputs to a system,

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which we defined as the input of new reactive N (Q) in this study. New nitrogen was

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also analyzed by Cui et al.,27 who calculated the reactive N balance for China’s

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terrestrial subsystem, and by Ma et al.,30 who studied flows of nutrient elements through

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Beijing. Cui et al.27 defined the new reactive N produced by biological nitrogen fixation

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(BNF), industrial nitrogen fixation (INF, which includes inorganic fertilizer application

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and production of chemical materials), atmospheric deposition, irrigation water, and

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seeds. They also used an indicator that they called “creation of reactive N”, which was

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derived from BNF, INF, inadvertent nitrogen fixation during fossil fuel combustion, and

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natural nitrogen fixation by lightning for the human-dominated Chinese mainland.

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However, they did not explicitly model the processes responsible for environmental and


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commercial inputs to and outputs from the system to regions outside of China, such as

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seawater exchange, air transport, and import and export by trade. In Ma et al.’s study30,

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new reactive nitrogen included fertilizer nitrogen, biological N fixation, atmospheric

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nitrogen deposition, and imports of nitrogen via livestock feed and human food that

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were not considered by Cui et al.27 They also accounted for phosphorus in these

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analyses30

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However, most other researchers referred to this as the input of reactive N. For

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example, national-scale research has included studies of Brazil,21 New Zealand,22 and

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China,28 and their nitrogen budgets were established based on the mass-balance model

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of Howarth et al.11 The nitrogen inputs in these studies came from BNF, fertilizer use,

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fossil fuel combustion, atmospheric deposition, and imports or net imports in human

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food and livestock feed. Urban-scale research included studies of the Central Arizona–

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Phoenix area of the United States (hereafter, “Phoenix”),20 Hangzhou24, and Shanghai25;

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the Phoenix study was used to guide the latter two studies. Compared with

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national-scale research, the nitrogen input terms in the urban-scale research were

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supplemented by adding chemical materials and riverine nitrogen inputs. In summary,

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one new contribution of the present study is that we also accounted for production of

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human food and livestock feed inside Beijing’s administrative boundary. The inputs of

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reactive N created by the consumption of human food and livestock feed were for the

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benefit of the resident population of the Beijing administrative region, whether the food

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and feed were from local production or from the external environment. Thus, it was

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reasonable to integrate local production with these terms to calculate the total input of

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reactive nitrogen.

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Recently, the percentage of the total nitrogen input accounted for by nitrogen in food



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has been increasing, thereby affecting the global biogeochemical nitrogen cycle. 31

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Leach et al.32 found that food nitrogen was the largest single component of the

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calculated nitrogen footprints of the United States and of the Netherlands. Faerge et al.33

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conducted an early study of an urban nutrient balance for Bangkok, Thailand, but

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treated the city as a black box when they analyzed its inputs and outputs rather than

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examining the flows within the box. Urban populations are highly concentrated per unit

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area, and the high nitrogen inputs associated with urban areas can cause these areas to

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become nitrogen hot spots.24,

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environmental impacts requires us to “open the black box” to examine details of how

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the system functions. Barles36 initiated a study of flows of food nitrogen within the

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urban system, including nitrogen in food after processing, the loss of nitrogen in

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agriculture, and the recycling of human and animal wastes. Ma et al.37, 38 established the

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NUFER (NUtrient flows in Food chains, Environment and Resources use) model from

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the perspective of an ecological food chain to detail the role of food production and

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consumption relative to the fate of wastes.

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Thus, understanding how to mitigate their

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In the abovementioned studies, most researchers treated the urban system’s

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socioeconomic sectors as a black box, and only considered nitrogen exchanges with the

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natural environment or focused on major nitrogen metabolic processes, such as those

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within the food system. They did not describe the overall processes from the perspective

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of metabolic research. In 1965, Wolman39 proposed the concept of urban metabolism,

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which subsequently provided a framework for research on the urban nitrogen

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metabolism. In this approach, researchers treat a city as analogous to a giant organism,

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and analyze the metabolic processes that arise from natural and anthropogenic inputs,

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circulation, and outputs of nitrogen. Beginning in the early 21st century, increasing


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numbers of researchers have focused on urban nutrient metabolism, and especially on

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the urban metabolic processes that involve flows of nitrogen and phosphorus.33 Forkes40

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studied the urban food metabolism of Toronto, Canada, with a focus on environmental

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inputs and outputs, but did not consider flows between socioeconomic sectors within

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the city; thus, like most previous studies, their study didn’t evaluate nitrogen metabolic

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processes holistically, from a systems perspective. Villarroel Walker and Beck41 and

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Villarroel Walker et al.42 focused on the socioeconomic sectors within an urban area and

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used a multi-sectoral analysis to calculate the flows of materials and elements among

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five industrial sectors. Around the same time, Wang and Lin43 and Lin et al.44 studied

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metabolic paths and metabolism-related flows of nutrients within the food system of

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Xiamen, China.

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In these studies, the authors viewed the natural environment as the source of support

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for the city’s complex socioeconomic system, but they did not account for every aspect

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of the system or for the relationships between components as a network; as a result, they

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did not reveal the transfers among the system’s components or the mutual support

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provided by these transfers in terms of the coupling between human systems and natural

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ecological systems. In addition, they only considered direct (adjacent) paths for nitrogen

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flows between pairs of components of the urban system; thus, they ignored the

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potentially large indirect (distal) flows between pairs of components that pass through

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one or more intermediate components. This accounting for indirect flows is important,

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because these flows can represent such a large proportion of the total flows that they

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should neglected45; doing so would provide a highly misleading view of the overall

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system. In the present study, we attempted to solve these problems with previous

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research by providing a more holistic view.



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Fortunately, there are approaches that let us examine all paths, including the indirect

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paths. Ecological network analysis is an effective method for identifying direct and

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indirect flows and performing an accounting analysis that provides a more holistic view

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of the system.46, 47 Hannon48 first applied economic input–output analysis (the Leontief

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model) to simulate the structural distribution of ecosystem components and the

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interrelationships among trophic levels. This method can be used to quantify the path

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structure of a system49,

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relationships among the components of the system50, 51. Although ecological network

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analysis has been widely applied in studies of natural ecosystems, its application to

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analyze socioeconomic systems has been more recent and less frequent52-56. When used

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for such systems, most applications have focused on single-sector or single-factor

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analysis, such as studies of specific industrial systems57-59, the household sector60,

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water61-63, energy64,

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studied societal metabolic systems using a multi-sector, multifactor form of societal

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metabolic processes.68 Based on the successes of this previous research, we selected

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ecological network analysis for the present study because it can effectively couple the

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socioeconomic system with the natural ecological system and permit an in-depth

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analysis of the processes involved in Beijing’s nitrogen metabolism.

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, the characteristics of the ecological flows46,

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, and the

, sulfur66, and phosphorus67. Recently, some researchers have

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The aim of our study was to identify the key nodes in the urban system (i.e., the ones

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that have more frequent relationships with other nodes), whether these are

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socioeconomic sectors or the natural ecosystem, and the key pathways between these

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components of the overall system (those that have the largest nitrogen flows). We also

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identified the key upstream and downstream relationships among the nodes and

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quantified their contributions to the system.


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2. Methods and data

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In this study, we obtained data for Beijing from 1996 to 2012 from various published

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government sources. The technical framework for this research is described in

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Supplemental Figure S1.

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2.1 Network modeling for Beijing’s nitrogen metabolism

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Based on the available data and a rational classification of urban nitrogen metabolic

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process, we defined 16 nodes in the metabolic system that exists within Beijing’s

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municipal administrative boundary, and we treated areas outside of Beijing’s municipal

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administrative boundary, including both socioeconomic and natural systems, as the

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external environment (node 0). It’s important to note that in contrast with most Western

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cities, Chinese cities are defined based on the municipal administrative boundary, which

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includes both built-up areas and rural areas (e.g., farmland and natural components).

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The first 11 nodes relate to socioeconomic components of the system and represent the

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major sources of anthropogenic nitrogen: Household (node 1), Pets (2), Industry (3),

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Animal Husbandry (4), Crop Cultivation (5), Aquaculture (6), Forestry (7), Services (8),

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Construction (9), Transportation (10), and Sewage Treatment (11). The remaining 5

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nodes represent the predominantly natural components of the system: Surface Water

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(12), Atmosphere (13), Forests (14), Grassland (15), and Farmland (16). We developed

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a network model of these nodes for Beijing (Supplemental Figure S2).

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2.2 Direct flows in Beijing’s nitrogen metabolic network

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In the network model, path fji represents a flow of nitrogen from node i to node j.

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These direct flow paths form the direct-flow matrix F. Inputs from and outputs to the

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external environment (node 0) from node i are given by zi0 and y0i, respectively. The



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flows included in these calculations include the inputs of food and livestock feed from

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the external environment; the production and consumption of the main agricultural,

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livestock, and poultry products; the application of fertilizers; industrial nitrogen use; the

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consumption of energy; and the discharge and treatment of pollutants within the

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administrative boundary of Beijing. We also included the amounts of nitrogen in the

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processes of biological nitrogen fixation, volatilization, denitrification, nitrogen

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deposition, and runoff. Because the flow data are often reported using different units of

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measurement, it was necessary to convert all of the amounts into a total mass of

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nitrogen N in units of Gg (1 Gg = 109 g). Supplemental Table S1 summarizes the

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equations used to calculate each flow; Supplemental Table S2 lists the individual

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accounting items and data sources, specific values for the nitrogen contents of materials,

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and coefficients for all of the inputs and outputs from each node.

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In the nitrogen flow accounting, the input of reactive N included new reactive N,

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which represents nitrogen obtained from the atmosphere or nitrogen-containing

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materials used in production and consumption to support human activities, and recycled

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reactive N, which represents the wastes reused by humans or discharged to the

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environment to participate in natural cycles after production and consumption activities.

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The input of new reactive nitrogen is an important indicator that has been studied by

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many researchers27, 30; it is denoted Q in this study. This indicator includes inputs of

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nitrogenous substances from the external environment (Z0 = ∑zi0), the consumption of

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nitrogenous substances inside Beijing’s administrative boundary, and the amounts of

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natural nitrogen fixation and atmospheric deposition. The natural reactive nitrogen (Qn)

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includes forest biological nitrogen fixation, grassland biological nitrogen fixation, and

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atmospheric deposition; anthropogenic reactive nitrogen (Qa) includes agricultural


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biological nitrogen fixation and the consumption of nitrogenous substances from the

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environment inside Beijing’s administrative boundary and the external environment.

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The consumption of nitrogenous substances includes food, livestock and aquaculture

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feed, fossil fuel combustion to provide energy, fertilizer, and chemicals. The formula for

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Q is as follows:21

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Q = BNFf + BNFg + D + BNFa + Z0 + LP

(1)

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where BNFf represents forest biological nitrogen fixation; BNFg represents grassland

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biological nitrogen fixation; D represents atmospheric deposition; BNFa represents

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agricultural biological nitrogen fixation, including nitrogen fixation by seeds and crops;

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Z0 represents the total input of nitrogenous substances from the external environment;

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and LP (local production, within Beijing’s administrative boundary) represents the

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production of nitrogenous substances inside Beijing’s administrative boundary. To

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describe the relative importance of anthropogenic and natural reactive N, we defined the

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index P, which is defined as P = Qa/Qn.

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2.3 Integral flows in Beijing’s nitrogen metabolic network

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Using the flow analysis methods from ecological network analysis,69 we calculated

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the dimensionless integral flow intensity matrix N', which can help clarify the flow

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distribution within the ecological network and the relative importance of direct and

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indirect flows:

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N'  (n'ij )  G'  G'   G'  G'  ... G'   ...  (I - G')-1

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where G’= (g’ij) is the direct flow intensity matrix, I is the identity matrix, the

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self-feedback matrix (G’)0 reflects flows that originate in and return to a node, (G’)1

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represents direct flows between two nodes, and (G’)m (m≥2) reflects the indirect flows

0

1

2

3

m


(2)


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of length m between nodes.

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The integral flow intensity matrix N' can be re-dimensionalized by post-multiplying

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by the diagonalized throughflow vector, diag(T), to transform the column of N', thereby

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producing the dimensional integral flow matrix Y: 70, 71.

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Y  diag(T)N'

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We also defined an index (I) which is the proportion of the total flows in the network

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accounted for by the sum of the input and output flows for a given node. For any node a

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among the n nodes:

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(3)

n

n

j 1

i 1

n

n

I = ( yaj   yia ) /  yij

(4)

i 1 j 1

If node a has flows that fluctuate over time, we use I ' = (

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compare flows from year to year.

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3. Results

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3.1 Analysis of direct flows

n

n

j 1

i 1

 yaj   yia ) to

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Beijing’s total input of new reactive N (Q) increased from 431 Gg in 1996 to 507 Gg

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in 2012. Q therefore increased to 1.2 times its value at the start of the study period. As

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natural reactive N (Qn) constantly decreased, the main contribution to the increase was

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from anthropogenic reactive N (Qa), whose proportion of Q exceeded 80%.

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Anthropogenic reactive N also increased to 1.2 times its initial value. The main sources

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of anthropogenic reactive N were the consumption of energy, food, and fertilizer. Since

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fertilizer consumption in 2012 decreased to half of its level in 1996, the main

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contributions to the increase of anthropogenic reactive N were energy and food


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consumption, which increased to 1.6 and 2.0 times their 1996 levels, respectively. In

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addition, the proportion of new reactive N obtained from the external environment (Z0)

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increased from 16.2% of the total to 86.0%, and its quantity increased to 1.3 times the

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1996 level. The main reason for the increase in Z0 was increases for the Household

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(node 1) and Farmland (node 16) components, which increased to approximately 8.4

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and 6.3 times their 1996 values, respectively. In summary, Z0 strongly affected Q, which

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reflects Beijing’s increasing dependence on inputs of material from its external

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environment.

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To improve our understanding of the inputs of reactive N to the system, we shifted

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our perspective to examine the internal network and identify the key direct paths for

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flows of nitrogen. We used the following principle for screening the key flows: we

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chose the nodes and paths with flows greater than 30 Gg, which represented at least 10%

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of Z0 in all years. Supplemental Table S3A contains the direct flow matrices for each

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year in our study, and Fig. 1 illustrates the relative magnitudes of these flows.

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Combining these data reveals that the number of paths with a direct flow >30 Gg

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increased throughout the study period. The number of such paths increased from 7 in

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1996 to 10 in 2012, and the proportion of the total paths accounted for by these paths

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with a high flow increased from 13.5% in 1996 to 19.2% in 2012. The number of paths

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whose flow was between 10 and 30 Gg decreased continuously. The proportion of the

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total paths accounted for by these paths decreased from 17.3% to 13.5%. The flows in

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most of the paths in the network were between 0 and 10 Gg, and these flows accounted

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for 66% of the total paths. Moreover, the highest flow was 7 orders of magnitude larger

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than the smallest flow.

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Insert Figure 1



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Overall, the number of output paths was highest for Industry (node 3), whose total

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output was also the largest. The largest recipient of flows from Industry was the

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Atmosphere (node 13), but this direct flow decreased from 94.1 Gg to 34.5 Gg,

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reflecting a large rapid reduction in fossil fuel combustion during industrial production

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that occurred during the study period. The Atmosphere (Node 13) received nitrogenous

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gases (e.g., NOx, NH3) from Industry (node 3) and from Farmland (node 16) as a result

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of volatilization and denitrification of inorganic and organic fertilizer. The direct flow

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from Farmland (node 16) to the Atmosphere (node 13) also decreased, from 78.2 Gg to

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43.81 Gg in 2012. The Atmosphere (Node 13) had both a larger input and a larger

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output by the end of the study period. This resulted primarily from the gain of nitrogen

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from the Atmosphere (node 13) by Forests (node 14) as a result of biological nitrogen

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fixation and atmospheric deposition. This flow changed little during the study period,

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remaining close to 45 Gg. Moreover, the direct flow from Crop Cultivation (node 5) to

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Animal Husbandry (node 4) was consistently greater than 30 Gg, reflecting the

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importance of livestock crop feed.

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Several other direct flows were small at the beginning of our study period, but

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increased to values greater than 30 Gg. For example, the direct flow from Industry

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(node 3) to Household (node 1) increased from 11.0 Gg to 52.0 Gg because of the

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greater consumption of manufactured products and energy. At the same time, the

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amount of wastes became larger. As a result, the direct flow from Household (node 1) to

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the Sewage Treatment (node 11) and the Atmosphere (node 13) increased to 5.9 and 4.8

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times their 1996 level, respectively. Moreover, as a result of a rapidly increasing number

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of vehicles, the direct flows from Industry (node 3) to Transportation (node 10) and

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from Transportation to the Atmosphere (node 13) both increased to approximately 5.3


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times their 1996 level.

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3.2 Analysis of integral flows

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Fig. 2 summarizes the integral flows, which include both direct and indirect flows. In

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contrast with the direct flows, we found many more large integral flows. The number of

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paths increased from 52 in the direct flow network to 171 after adding indirect flow

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paths. All integral flows were greater than or equal to the direct flows. Table S3B

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provides the actual values of the integral flows. The number of paths whose integral

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flows were >30 Gg remained at 26, representing 15% of the total number of paths. This

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proportion was equal to that in the direct flow network, but the number of integral paths

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was more than 3 times the number of direct flow paths. In contrast with the direct flows,

358

the proportion of the total number of paths accounted for by paths with flows between

359

10 and 30 Gg increased from 8.2% in 1996 to 13.5% in 2012. Most of the paths

360

continued to have flow values between 0 and 10, which accounted for about 72% of the

361

total number of paths. Moreover, the largest integral flow was 6 orders of magnitude

362

greater than the smallest integral flow.

363

Insert Figure 2

364

Overall, the Atmosphere (node 13) was clearly related to all paths with a flow greater

365

than 30 Gg, and its I value was 29%. The largest supplier of integral flows of nitrogen

366

was Industry (node 3), which accounted for 27% of the total inputs of the Atmosphere

367

(node 13). However, there was no obvious change in this proportion during the study

368

period and the integral flow from Industry (node 3) to Atmosphere (node 13) remained

369

roughly constant at 200 Gg, which differs from the downward trend for direct flows.

370

Farmland (node 16) also had a large integral contribution to the Atmosphere (node 13).



371

However, this flow decreased from 113.0 Gg in 1996 to 73.0 Gg in 2012, clearly

372

fluctuated during this period, and decreased to an integral flow equal to 64.6% of the

373

flow in 1996. This trend was the same as for its direct flows; the Atmosphere (node 13)

374

had both a greater input and a greater output, primarily via the integral flow from the

375

Atmosphere (node 13) to Forests (node 14), which accounted for approximately 12% of

376

the total output of the Atmosphere (node 13). The difference between this integral flow

377

and the direct flow was very small. Moreover, their inter-annual variation was low. The

378

larger integral flows along the paths surrounding the Atmosphere were also evident in

379

the direct flow network.

380

Moreover, the integral flow along the paths surrounding Household (node 1) was also

381

remarkable and its I value was 15%. The main contributions to the integral input of

382

Household (node 1) were from Industry (node 3) and Crop Cultivation (node 5).

383

Industry (node 3) contributed approximately 16% of the total integral inputs of

384

Household (node 1). This flow showed no obvious changes over time, in contrast with

385

its direct flows, which grew during the study period. The integral flow from Crop

386

Cultivation (node 5) to Household (node 1) decreased from 78.9 Gg in 1996 to 43.7 Gg

387

in 2004, and then increased to 52.8 Gg by 2012. Although the trend for the integral

388

flows was similar to that of the direct flows, the rate of change was smaller. The

389

abovementioned results show that Industry (node 3) was the main supplier to other

390

nodes, as the number of output paths was greatest from Industry (node 3), and its output

391

was also the largest in the integral flow network. The sum of the total integral inputs

392

and outputs of Industry (node 3) accounted for approximately 31% of the integral flows

393

based on the total number of paths. Moreover, Animal Husbandry (node 4) also showed

394

a large integral input, mainly contributed from Crop Cultivation (node 5) and Farmland


Page 18 of 36

Page 19 of 36

395


(node 16).

396

The integral flows that showed a clear trend were of obvious interest. Some of these

397

flows were small at the beginning of the study period, then increased to values greater

398

than 30 Gg. Nodes that had the greatest growth of integral flows included Sewage

399

Treatment (node 11) and Transportation (node 10), whose I’ values increased to 3.7 and

400

5.2 times their 1996 values, respectively. The integral flow from Household (node 1) to

401

Sewage Treatment (node 11) increased to approximately 5.9 times the 1996 level. The

402

integral flows from Industry (node 3) to Transportation (node 10) and to Atmosphere

403

(node 13) increased to 5.6 and 5.1 times the 1996 level, respectively.

404

4. Discussion

405

Using the analogy with a biological metabolism to study the interactions among the

406

components of an urban metabolism has proven to be an effective way to identify

407

disorders in an urban metabolism. The research objects in urban metabolism included a

408

city’s material metabolism,

409

carbon metabolism.77 Since the 21st century, more and more researchers focused on

410

urban nutrient metabolism.33, 40

72, 73

water metabolism,74,

75

energy metabolism,76 and

411

It is helpful to compare the present results with the results obtained by other

412

researchers, because such comparisons provide insights into both systems. We

413

compared the percentage of anthropogenic reactive N (Qa) and natural reactive N (Qn)

414

with the values from two previous studies of Chinese cities (Hangzhou and Shanghai)

415

as well as a study of Phoenix. We also compared the values of these indicators at a

416

national scale for China, Brazil, and New Zealand to explore whether different rules

417

governed the inputs of new reactive N at national and urban scales. Table 1 summarizes



418

the results. We found that the ratios of anthropogenic to natural reactive N (P) at the

419

urban scale were all at least 3.3 at an urban scale. In contrast, although the P value for

420

China was 4.0, the P values for Brazil and New Zealand were an order of magnitude

421

lower. This is probably due to the abundant natural resources of these two countries and

422

the resulting high capacity for natural nitrogen fixation. Overall, the value of P at the

423

urban scale was greater than that at the national scale. This is probably because the

424

biological nitrogen fixation capacity is relatively weak at an urban scale and the effects

425

of human activities on the nitrogen cycle are more obvious; at national scales, there are

426

larger natural areas and thus, a higher biological nitrogen fixation capacity.

427

Insert Table 1

428

In our comparison among the cities (Table 1), we found that Shanghai had the

429

greatest ratio of anthropogenic to natural nitrogen (as measured by its P value), at more

430

than 3 times Beijing’s value, whereas the ratio for Phoenix was almost equal to that of

431

Beijing and that of Hangzhou was smallest, at roughly 25% of Shanghai’s value. These

432

findings mean that the proportion of total reactive N accounted for by anthropogenic

433

reactive N was greatest for Shanghai, whose value was dominated by fertilizer

434

consumption (31%) and fossil fuel combustion (43%). Shanghai’s P value was also

435

relatively high because the rapid expansion of urban land has resulted in an equally

436

rapid decrease in the area of natural ecological land. The forest cover in Shanghai

437

decreased from 5.5% in 199067 to 3.2% in 2004.77 Thus, the natural nitrogen fixation

438

ability weakened rapidly. Beijing’s and Phoenix’s P values were both about 4.4, so their

439

proportions of total reactive N accounted for by anthropogenic reactive N were also

440

relatively high. Jenerette and Wu78 found that since the beginning of the 20th century,

441

Phoenix has experienced substantial land transformation, changing from an agricultural


Page 20 of 36

Page 21 of 36


442

area to an urban area. Both the human population and the urbanized area in this region

443

have increased exponentially, and the two parameters were strongly positively

444

correlated (r = 0.95). In all, 77% of the land area in Phoenix is desert and undeveloped,

445

resulting in a low capability for natural nitrogen fixation. Hangzhou had the highest

446

proportion of total reactive N accounted for by natural reactive N. This is because

447

Hangzhou’s forest cover reached 62.8% in 2004,79 which is approximately 2.9 times

448

that of Beijing. The forest restoration program conducted by Hangzhou is an attractive

449

option for cities such as Beijing that have undergone rapid urbanization and thereby

450

reduced their capacity to absorb nitrogen.

451

During the study period, urbanization of Beijing has been rapid, and this has greatly

452

increased the city’s population. Population growth motivated increasing nitrogen inputs

453

to a city. Moreover, changes in living habits also contributed to the increase of nitrogen

454

input. For example, an obvious structural transformation has occurred in the nature of

455

food consumption in China as a result of rapid socioeconomic development and

456

increasing standards of living. Food consumption patterns in Beijing have shown an

457

increase in the percentage of animal products (by nitrogen mass) from 30.9% of the

458

total in 1996 to 43.3% in 2012. Ma et al.30 found an even stronger trend in Beijing from

459

1978 to 2008, when the percentage of animal products increased from 8% to 41% (by

460

nitrogen mass).

461

The key achievement of this study is that it demonstrates how ecological network

462

analysis can provide important insights into the flows of an element (here, nitrogen)

463

through an urban system. These insights can then guide management of that element to

464

improve the efficiency of its use and decrease its environmental impacts. Specifically,

465

our results show that Beijing’s increasing population and changes in food consumption



466

patterns (increased consumption of animal products due to gradually improving

467

standards of living) have had a major influence on nitrogen flows. Since it is unlikely

468

that residents will decrease their consumption of animal protein, this means that the

469

city’s managers will have to emphasize ways to make the production of animal protein

470

more efficient and recapture (recycle) more of the nitrogenous wastes generated by this

471

production and consumption. However, Beijing’s local food supply has been decreasing

472

for both animal products and agricultural products. The demand for feed and fertilizer

473

has also decreased because more of these products are produced outside the city’s

474

administrative boundaries. . This is contrary to the example of Xiamen, China43. In

475

addition, despite efforts to adopt cleaner forms of energy, Beijing’s combustion of fossil

476

fuels remains too high, and managers must find ways to reduce this consumption (e.g.,

477

further increase the use of green energy) or recapture more of the emitted nitrogenous

478

gases.

479

Despite the important insights that our study provided into Beijing’s nitrogen

480

metabolism, our study has some limitations, particularly in terms of uncertainties in the

481

data. First, it can be difficult to apply ecological network analysis to an urban

482

socioeconomic system. The inputs may not equal the outputs for each node (i.e., there

483

may not be a “conservation of mass” or the model may not account for nitrogen storage

484

within a node) because of inaccuracies in the available data, and we treated this

485

difference as either a dissipative property (net output) or an accumulation (net input). In

486

future research, it will be necessary to find ways to decrease such errors, but this will

487

always depend on the quality of the available data. Second, our accounting method did

488

not consider some smaller-scale processes, such as leaching of fertilizers and other

489

nitrogen sources into bodies of water, because the dynamics of natural nitrogen


Page 22 of 36

Page 23 of 36


490

circulation are complex, and inadequate data is available to model these processes.

491

Third, the values of the coefficients used in our analysis should be refined in future

492

research. For example, we used the nitrogen deposition coefficient for China rather than

493

a coefficient calculated specifically for Beijing. This will create errors due to

494

differences between the geographical and climatic conditions in Beijing (a relatively

495

cold and dry northern city) and those in other areas of China (e.g., warm and wet

496

conditions in southern China). The problem is that there is insufficient data with

497

sufficiently high accuracy to allow such an analysis. We will try to decrease these

498

uncertainties in future research by obtaining Beijing-specific data and establishing an

499

appropriate extrapolation model. A final problem is that our results are highly

500

aggregated, and thus cannot lead us to provide specific quantitative recommendations

501

for specific metabolic activities. For example, to understand which specific industrial

502

activities should be modified, it would be necessary to de-aggregate the aggregated data

503

for the Industry component into estimates for the dozens of industries that make up this

504

component. This remains a challenge for future research.

505

Acknowledgements

506

This work was supported by the Fund for Innovative Research Group of the National

507

Natural Science Foundation of China (No. 51421065), by the Program for New Century

508

Excellent Talents in University (No. NCET-12-0059), by the National Natural Science

509

Foundation of China (No. 41171068, No. 41571521), by the Fundamental Research

510

Funds for the Central Universities (No. 2015KJJCA09), and by National key research

511

and development plan (No. 2016YFC0503005).



512

Page 24 of 36

Supporting Information

513

The technical framework (Figure S1). Network conceptual model (Figure S2).

514

Equations used to calculate the direct flows (Table S1). The values and sources of the

515

parameters used in the equations (Table S2). Direct and integral flow matrices (Table

516

S3).

517

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Fig. 1 The direct flows among the components of Beijing’s nitrogen metabolic network from 1996 to 2012. The green, orange, cyan, and gray represent flows of 60 to 100 Gg, 30 to 60 Gg, 10 to 30 Gg, and 0 to 10 Gg, respectively. Note that nitrogen flows from the external environment and self-feedback flows within a component have been omitted to simplify the diagrams. Nodes are: 1 = Household, 2 = Pets, 3 = Industry, 4 = Animal husbandry, 5 = Crop cultivation, 6 = Aquaculture, 7 = Forestry, 8 = Service, 9 = Construction, 10 = Transportation, 11 = Sewage treatment, 12 = Surface water, 13 = Atmosphere, 14 = Forest, 15 = Grassland, 16 = Farmland. zi represents inputs into node i from the external environment.


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741 742 743 744 745 746 747 748 749 750

Fig. 2 The integral flows among the components of Beijing’s nitrogen metabolic network from 1996 to 2012. Colors of green, orange, cyan, and gray represent flows of 60 to 100 Gg, 30 to 60 Gg, 10 to 30 Gg, and 0 to 10 Gg, respectively. Note that nitrogen flows from the external environment and self-feedback flows within a component have been omitted to simplify the diagrams. Nodes are: 1 = Household, 2 = Pets, 3 = Industry, 4 = Animal husbandry, 5 = Crop cultivation, 6 = Aquaculture, 7 = Forestry, 8 = Service, 9 = Construction, 10 = Transportation, 11 = Sewage treatment, 12 = Surface water, 13 = Atmosphere, 14 = Forest, 15 = Grassland, 16 = Farmland. zi represents inputs into node i from the external environment.



751 752 753 754

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Table 1 The values of anthropogenic new reactive nitrogen (Qa), natural new reactive nitrogen (Qn), and their ratio (P = Qa / Qn) for four urban-scale studies and three national-scale studies.

Urban scale

National scale New

Beijing

Phoenix

Shanghai

Hangzhou

China

Brazil Zealand

Year

2004

1998

2004

2004

2004

2002

2001

81

82

93

77

80

42

28

19

18

7

23

20

58

72

4.3

4.5

13.3

3.3

4.0

0.7

0.4

(20)

(25)

(24)

(15)

(21)

(22)

Qa (% of total Q)

1

Qn (% of total Q)

1

P (= Qa/Qn)

Presen Source t study 755 756 757

1. Using our definitions of anthropogenic and natural reactive N, we recalculated the percentages of anthropogenic reactive N (Qa) and natural reactive N (Qn) based on data from the other studies.

758


A Network Flow Analysis of the Nitrogen ... - ACS Publications

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