Nitrogen Footprint in China: Food, Energy, and Nonfood Goods

Jul 24, 2013 - Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada T6G 2E3. ¶. Research Center for Sustainable Develo...
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Nitrogen Footprint in China: Food, Energy, and Nonfood Goods Baojing Gu,†,‡ Allison M. Leach,§ Lin Ma,∥,⊥ James N. Galloway,§ Scott X. Chang,# Ying Ge,‡,¶ and Jie Chang*,‡,¶ †

Policy Simulation Lab, Zhejiang University, Hangzhou 310058, PR China College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China § Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia 22904, United States ∥ Department of Plant Nutrition, China Agricultural University, Key Laboratory of Plant-Soil Interactions, Ministry of Education, Beijing 100094, PR China ⊥ Department of Soil Quality, Wageningen University, Wageningen, P.O. Box 47, 6700 AA, The Netherlands # Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada T6G 2E3 ¶ Research Center for Sustainable Development, Zhejiang University, Hangzhou 310058, PR China ‡

S Supporting Information *

ABSTRACT: The nitrogen (N) footprint is a novel approach to quantify losses to the environment of reactive N (Nr; all species of N except N2) derived from human activities. However, current N footprint models are difficult to apply to new countries due to the large data requirement, and sources of Nr included in calculating the N footprint are often incomplete. In this study, we comprehensively quantified the N footprint in China with an N mass balance approach. Results show that the per capita N footprint in China increased 68% between 1980 and 2008, from 19 to 32 kg N yr−1. The Nr loss from the production and consumption of food was the largest component of the N footprint (70%) while energy and nonfood products made up the remainder in approximately equal portion in 2008. In contrast, in 1980, the food-related N footprint accounted for 86% of the overall N footprint, followed by nonfood products (8%) and energy (6%). The findings and methods of this study are generally comparable to that of the consumer-based analysis of the N-Calculator. This work provides policy makers quantitative information about the sources of China’s N footprint and demonstrates the significant challenges in reducing Nr loss to the environment.

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INTRODUCTION

The N footprint related to industrial products is not considered in the N-Calculator. The N-Calculator defines all Nr lost to the environment during the food production process but not contained in the consumed food product, as “virtual N”.12 The data demand for this consumer-based approach is high to calculate detailed virtual N factors (total virtual N divided by food consumption N) for various food products,9 and variations of the virtual N factor can be large among different countries/regions. Thus, it is not appropriate to apply this consumer-based, bottom-up approach globally with one suite of virtual N factors. Therefore, it is essential to develop a revised or an alternative approach to resolve the above-mentioned shortcomings that previous models did not resolve. The primary purposes of this study are to develop a new national N footprint model using an N mass balance approach in the coupled human and natural systems (CHANS) and to then use that tool to estimate China’s N footprint. CHANS

Human activities have substantially altered the global nitrogen (N) cycle, in particular in increasing reactive N (Nr; all species of N except N2) release to the environment,1,2 which currently has exceeded the safe operating limit and has brought a huge burden for the Earth system.3 To quantify these burdens, the ecological footprint4−6 and carbon footprint7,8 concepts have been developed to show how human activities impact the Earth system. Similarly, the N footprint framework (the N-Calculator, http://www.n-print.org/) was developed to provide a consumer-based tool to determine an individual’s contribution to N losses to the environment from resource uses.9 However, research on the N footprint is still limited,10,11 and the sources of Nr included in calculating the N footprint are incomplete.9 In the consumer-based N footprint model,9 three components are considered: food production, food consumption, and combustion of fossil fuels for domestic use and provision of goods and services. One advantage of the consumer-based NCalculator is that food production practices can be made specific to a country or a region.9 This approach also connects an individual consumer with Nr losses to the environment, showing how changing behavior patterns can alter Nr losses. © 2013 American Chemical Society

Received: Revised: Accepted: Published: 9217

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Figure 1. Nitrogen cycle in the CHANS used to calculate the nitrogen footprint. DON = dissolved organic N. The colors of the backgrounds represent different functional groups: blue = life-supporter (including atmosphere and hydrosphere); green = processor; red = consumer; gray = remover. Data represent the N fluxes in 2008 with unit Tg N. Arrows represent N flows, which are all included in the calculation of the N footprint in this study, with the exception of the gray arrows. Green arrows represent N flows associated with agricultural production; black arrows represent N fixation; orange arrows represent N flows associated with human consumption; red arrows represent N emission to atmosphere; blue arrows represent N discharge to the hydrosphere; gray arrows mainly represent natural N processes and others that are not related to the calculation of the N footprint. For the subsystem level calculation, the N footprint is calculated by adding all the N fluxes in red and blue arrows and denitrification potential. If these red and blue arrows are from the processor functional group, they represent the virtual N during the production of food, nonfood products, and services. If these red and blue arrows are from the human subsystem, they represent the N footprint of human consumption. For the system level calculation, the N footprint is calculated by adding all the N fluxes in black arrows and the net N import to the CHANS that is not shown here. Urban green-land is reassigned to the consumer group owing to its close relationship with the human and pet subsystems as well as its nonproduct supply services.

covers and integrates all anthropogenic Nr fluxes, tracks the detailed sources of anthropogenic Nr fluxes, and helps to quantify the contributions of the different sources to the overall N footprint. In addition to the usual contributors to the N footprint (i.e., food consumption, food production, and fossil fuel combustion), we also include the N footprint related to industrial products (synthetic fiber, plastics, rubber, dye, explosives, drugs, etc.), which are increasing rapidly13,14 but are currently not included in the overall N footprint calculations. In many regions, industrial N fluxes are an important component of the overall N budget,13−15 and the production and consumption of these industrial products can lead to environmental and health problems.14 Therefore, it is essential to include industrial products in the N footprint framework, to support the overall effects analysis of human activities. We then discuss the differences between the methods used in this study and that in the consumer-based NCalculator.9 Finally, we assess the total national N footprint of China and provide some future perspectives.

systems are coupled via reciprocal interactions, such as material flows.16,17 We first conducted a material flow analysis 18 of N in the whole CHANS in China. All the N flows related to the N footprint calculation (e.g., Nr loss during production and after consumption) were then extracted from the material flow analysis (Figure 1). The system boundaries follow the country boundaries of China (see Supporting Information (SI) text for details). For this study, the CHANS is divided into four functional groups based on the mutual services of Nr among these groups (see Figures S1 and S2 in SI), which are further divided into 14 subsystems (see SI for details). These functional groups (and subsystems) are processor (cropland, forest, grassland, livestock, aquaculture, and industry subsystems), consumer (human, pet, and urban green-land subsystems), remover (wastewater treatment and garbage treatment subsystems), and life-supporter groups (near-surface atmosphere, surface water, and groundwater subsystems). Data Collection and Mass Balance Calculations. Data used in this study were mainly derived from governmental statistical yearbooks and bulletins,19 which represent the best available data for the quantification of the anthropogenic N footprint in China (detailed data sources can be found in Table S2, SI). In addition, data from published articles were also used for comparison (e.g., ref 20). All data can be divided into two

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METHODS Material Flow Analysis. To quantify the N footprint, the CHANS approach was adopted in this study. The CHANS approach is an explicit acknowledgment that human and natural 9218

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fossil fuel combustion in different sectors that supports the domestic consumption, including transportation, domestic heating and electricity usage, and goods and services, as the final energy footprint (see SI for details of calculation). The second approach was based on the N balance of the whole CHANS (at the system level). The system level N footprint (NFsystem) in a certain year is calculated as:

categories: (1) basic information of China, such as population, gross domestic product (GDP), land use, fertilizer usage, and crop/livestock production, which is mainly taken from the statistics of yearbooks,19 and (2) coefficients used for the calculation of N fluxes, such as the rate of biological N fixation, the rate of denitrification, and the rate of excretion generation by livestock; such information was mainly obtained from the literature. The N balance calculations of the CHANS whole system, functional groups, and subsystems followed the basic principle:20−22 m

n

NFsystem = NBNF + NHBNF + NFFC + NNNI

where NBNF is biological N fixation (BNF), NHBNF is HaberBosch N fixation (HBNF), NFFC is fossil fuel combustion, and NNNI is net Nr import from other countries. The BNF is included in the calculation only when it is related to the production of food or nonfood goods. For instance, the BNF of natural forests is not considered; however, it is considered if the forest is used for wood production. The NFsystem may introduce uncertainty to the N footprint since a country usually imports/ exports a large proportion of its food and nonfood goods from/ to other countries. Although the Nr contained in these exported products can be reflected in NNNI, the virtual N related to these products would result in uncertainty. Therefore, we also used the export proportion9 to refine the final results of the NFsystem. Methods Compared with Other Studies. The subsystem N footprint calculation approach in this study was then compared to the main N footprint model, the consumer-based N-Calculator.9 The subsystem approach in this study can be compared to the primarily bottom-up, consumer-based approach of the N-Calculator.9 This comparison is valid for both the N footprint derived from food and energy consumption and for the N footprint from virtual N, despite calculation differences for the latter. In the N-Calculator, virtual N factors describing average food production in the United States were used to calculate the virtual N; however, in this study, the virtual N was directly calculated from the N balance with annual data on N cycling for a country. The N-Calculator virtual N factors represent the average Nr losses during the production of N-containing products, such as major food categories, making them unaffected by the variations in N cycling, e.g., the different contributions of fertilizer, manure, and other N inputs for crop production that can change the virtual N factors in different years. In contrast, the virtual N calculated by the N balance in this study varies with the real situation of N cycling because it uses reported fertilizer and N cycling statistics. This means that if we also used virtual N factors to represent the Nr loss calculated in the N balance method, the virtual N factors would change across different years to account for changes in N cycling, although these changes may be small. Despite these differences in virtual N calculation, these two methods are comparable because the fundamental principle is the same; i.e., the N footprint represents the losses to the environment of Nr derived from human activities. In this study, the system level method is used to cross check the results from the subsystem approach in this study, which is similar to the cross check in Leach et al.9 Both cross checks consider the total anthropogenic Nr input from outside of a country, and the international import/export data are used to refine the estimates. In Leach et al.,9 the bottom-up NCalculator N footprint was validated by the cross check calculation for the United States. Similarly, the subsystem and system levels approaches in this study were also comparable to each other. However, the system level method in this study was

p

∑ INh = ∑ OUTg + ∑ ACCk h=1

g=1

(1)

k=1

where INh and OUTg represent the different N inputs and outputs, respectively, and ACCk represents the different N accumulations (detailed calculations can be found in the SI). The N inputs and cycling refer to Nr1 that includes organic N, ammonium (NH4+), NH3, NOx, N2O, and nitrate (Figure 1). We used the N Cycling Network Analyzer (NCNA) model to compile the data set and calculate all N fluxes as total Nr.23 This model standardizes the parameter collections for the N flux calculations and automatically calculates the N fluxes and their interactions based on the mass balance approach (Figure S3, SI). Nitrogen Footprint Calculation. Similar to Leach et al.,9 the N footprint is defined as the total amount of Nr released to the environment as a result of an entity’s resource consumption, expressed in total units of Nr. The major components included in this study are food consumption, food production, fossil fuel combustion, and the production and consumption of nonfood goods. The data methods described above were then used to calculate the N footprint of China using the following two approaches. In the first approach, the N footprint of food and nonfood goods (NFgoods) in a certain year is calculated at the subsystem level: n

NFgoods =

n

∑ NFgoods

i

i=1

=

∑ (Nvirtual

i

+ Nconsumptioni)

i=1

(2)

Nvirtual = NVair + NVwater + NVsolid

(3)

Nconsumption = NCair + NCwater + NCsolid

(4)

(5)

where Nvirtual is virtual N, which represents all Nr lost to the environment during the production of food or nonfood goods that is consumed in a country; Nconsumption is Nr discharged to the environment via human consumption; i is the consumption of different types of food or nonfood goods, such as rice, pork, synthetic fiber, etc.; NVair is the Nr loss to the atmosphere during production, including NH3 volatilization, NOx emission (mainly straw burning; fossil fuel burning is considered in energy N footprint), and denitrification; NVwater is the Nr loss to the hydrosphere during production, including runoff to surface water and leakage to groundwater; and NVsolid is the Nr loss as solid waste, such as the landfill of straw waste or food processing waste. Similarly, we calculate the Nconsumption by summing the Nr loss to air, water, and solid waste during the consumption of food and nonfood goods. All these Nr loss fluxes are extracted from the material flow analysis (Figure 1), on the basis of the N balance of each subsystem in the CHANS. For the energy N footprint, we used the NOx emission from 9219

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Figure 2. Nitrogen footprint in China and other countries. (a) the N footprint in China derived from two different methods; (b) comparison of the N footprint between the two methods; (c) source appointments of the N footprint via subsystem level estimates. Goods include the agricultural source (e.g., cotton, leather, wool) and industrial source (e.g., synthetic fiber, dye, drug). Aq_v = aquaculture virtual N, Ls_v = livestock virtual N, Cl_v = cropland virtual N, and Goods_v = goods virtual N. Energy represents the NOx−N emission via fossil fuel combustion. (d) Comparison of the N footprint in China, the United States, and The Netherlands. The data for the United States and The Netherlands were from Leach et al.9.

not compared to the Leach et al.9 cross check approach because this paper focuses on the subsystem N balance approach.

(NUE) from 6 to 17% (Figure S4, SI). This change may be related to the rapid socioeconomic development since the implementation of Reform and Opening-up policy in the early 1980s. First, the proportion of livestock that supplies raw power, such as for tillage and transportation, substantially decreased with the increased usage of modern machineries in agriculture in China.25 This increased the production of animal products while using the same amount of feed. Second, the livestock composition changed. The proportion of hog to total livestock production (quantified as Tg N yr−1) was reduced from 60 to 39% during the period 1980−2008, while poultry and milk production increased from 30 to 47% of total livestock production during the same period.19 The changes from hogdominated to poultry and milk-production dominated operations largely increased the NUE of livestock because the feed conversion ratio (FCR) of chicken and milk was about 3 times greater than that of pork.24,26 Third, the overall FCR increased in China. The increased grains that were used for livestock feed 27 advanced the development of industrial farms that substantially increased the NUE in livestock production. For the aquaculture N footprint, the total consumption and the Nr loss during production only accounted for less than 2% of the total N footprint in China (Figure 2c). The aquaculture products were still not a main component of China’s food consumption, and over 30 (in 2008) to 70% (in 1980) of aquaculture products were harvested through fishing of natural stocks.19 The Nr loss during this process comes from energy consumption, e.g., during transportation, natural fishing, and waste processing in natural fishing. Although the per capita food consumption varied between 1980 and 2008, it was generally around 5 kg N yr−1, but only 0.7 (in 1980) to 3.7 (in 2008) kg N yr−1 was lost to the environment after food consumption, because the consumed N

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RESULTS AND DISCUSSION N Footprint in China with Subsystem Level Estimates. On the basis of subsystem level estimates, the average per capita N footprint in China increased over 50% from 19 kg N yr−1 in 1980 to 32 kg N yr−1 in 2008, in a linear fashion (Figure 2a). The food-related N footprint was the single largest component, increasing from 16 to 22 kg N yr−1 during that period (Figure 2c). The rapid increase of the food-related N footprint was mainly a result of the changed human diet. The per capita consumption of cereals was reduced from 4.5 to 3.5 kg N yr−1, while the per capita consumption of meat, egg, and dairy products increased from 0.5 to 1.5 kg N yr−1, between 1980 and 2008. This change increased the N footprint since more crops were used for the production of animal protein that would have increased the Nr loss alongside the food production chain.9,24 In 1980, the proportion of crops used as food in China was as high as 94%, while it was only 40% in 2008, with 60% of crop production used as feed for livestock or aquaculture productions. The virtual N of crop production in China increased rapidly from 9.5 to 15 kg N yr−1 (Figure 2c) as a result of the rapid increase of N fertilizer usage (9.4 to 24 Tg N, ref 19) from 1980 to 1996, after which the slower increases in fertilizer usage and the reduced usage of manure maintained the level of the total Nr input to China’s cropland. This reduced the amount of virtual N associated with crop production in China after 1996. For the livestock N footprint, although the consumption of animal products increased, the associated virtual N from livestock production decreased from 6.2 to 4.6 kg N yr−1 from 1980 to 2008 as a result of the increase of N use efficiency 9220

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(mainly human excretion) entered into the cropland as manure in China during that period. Given that there was limited advanced (or tertiary) sewage treatment that converts Nr into a nonreactive form (N2) in China,28,29 wastewater treatment did not contribute to the reduction of the N footprint.9 The nonfood products N footprint (both virtual and consumption, per capita) linearly increased from 1.6 to 5.1 kg N yr−1 between 1980 and 2008 (Figure 2c). Agricultural nonfood products, such as cotton, leather, and wool, contributed to over 50% of the total nonfood product N footprint before 2005; after that, industrial nonfood products, such as synthetic fibers, plastics, rubber, resins, explosives, and paints, became the main contributor to the nonfood product related N footprint. However, the consumption of nonfood products is different from that of food products because the Nr is not quickly released to the environment after the consumption of nonfood products such as synthetic fibers, which are one example of a structural product.14 In contrast, nonstructural products, such as explosives, drugs, pesticides, and reagents, would release the Nr to the environment once they have been used, similar to food-related products, causing immediate effects to the environment.14 Generally, 95% of agricultural nonfood products and about 70% of industrial nonfood products in China are structural products, which release Nr to the environment decades or centuries after being manufactured because of their slow rate of biodegradation.14 Therefore, the current distribution patterns and fluxes of structural products in the country would affect the Nr release in the future and cause legacy effects to the environment such as the cities besieged by garbage,16 leading to challenges in managing the N footprint in the future. Furthermore, some of the Nr released from nonfood products is associated with toxic substances, such as the hydrogen cyanide (HCN) released from the burning of polyacrylonitrile and polyurethane foam that is sometimes fatal to humans.14 Future research should consider the N footprint of nonfood goods, especially industrial goods. The energy N footprint, similar to that of nonfood related products, increased from 1.1 to 4.6 kg N yr−1 between 1980 and 2008 (Figure 2c). However, the energy N footprint had a different temporal pattern: (i) it increased by 1.5-fold from 1980 to 1995 as China’s GDP increased about 10% annually during that period;19 (ii) it then decreased from 1996 to 2000 owing to the Asian economic crisis and the decrease in the consumption of coal to total energy from 77 to 71%; and (iii) it sharply increased after 2000 as a result of economic recovery and increased coal consumption.30 Industrial energy consumption is the largest contributor to the energy N footprint, accounting for about 70% of the total energy N footprint (Figure 3); this was followed by housing (10−15%), transportation (5−8%), service and commercial (5%), and agriculture (2−5%) energy consumption. It is difficult to calculate the energy N footprint that was caused by the export of industrial products that should be subtracted from the total; therefore, the per capita energy N footprint in China reported here likely reflects an overestimate. If we use the CO2 emissions associated with goods exported from China as an estimate,31 China’s total energy N footprint would be reduced by about 7%. Cross-Check with System Level Estimates of the N Footprint in China. To cross check the reliability of the China’s N footprint calculations, a system level calculation was conducted. All the Nr creation in China, including BNF, HBNF, fossil fuel combustion, and international feed import,

Figure 3. Energy N footprint in China during the period of 1980− 2008.

was quantified and was shown to increase from 19 to 49 Tg N yr−1 between 1980 and 2008 (Figure 4). Fertilizer used for food

Figure 4. Nr input to China from outside the system. AG = artificial grassland; BNF = biological N fixation. Livestock fertilizer represents the urea used for straw ammoniation to produce feed. Other natural BNF was not included since it was not related to the anthropogenic N footprint.

production accounted for 60−70% of the total Nr creation in China (Figure 4), followed by BNF (15−30%), fossil fuel combustion (6−13%), and others. Expressed on a per capita basis, the N footprint in China during this period was between 19 and 37 kg N yr−1, slightly higher than the values estimated by the subsystem level method (19 to 32 kg N yr−1). The differences can be mainly attributed to the following two reasons: (i) uncertainty in the product export data; and (ii) some synthetic fertilizers were not used for the production of food or nonfood products, for instance, synthetic fertilizers used for the production of biofuel. To refine the system level estimate, we subtracted the Nr related to exported products (both product N and virtual N, ref 31) and the fertilizer that was not used for the production of food or nonfood products. The revised per capita N footprint is 19 to 33 kg N yr−1 from 1980 to 2008, which is essentially identical to the results obtained from subsystem level estimates (Figure 2b, R2 = 0.99, P < 0.001), suggesting that the mass balance approach worked very well for the N footprint calculation. Comparison of the China’s N Footprint with Other Countries. We compared the N footprint in China calculated with the CHANS subsystem level approach with that in other countries calculated with the N-Calculator (Figure 2d). The 9221

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comparisons in the following section are relative because different models were used for different countries, although these models are comparable. Currently, the per capita N footprint (only considering food and energy in order to be comparable with the N-Calculator) in China was slightly higher than that in The Netherlands (23 kg N yr−1) but much lower than that in the US (39 kg N yr−1). In the US, the per capita consumption of food and the proportion of animal protein was much higher than in China,32 leading to a larger food-related N footprint in the US than in China.9,29,33 The Dutch also consumed more food and more animal protein than the Chinese.32 However, advanced sewage treatment removed about 78% of the food N footprint resulting from food consumption in The Netherlands, leading to a relatively lower total food related N footprint (Figure 2d). The per capita energy N footprint was higher in China than in The Netherlands but was lower than in the US (Figure 2d). There are two factors that affect the energy N footprint: NOx emission per unit energy consumption and per capita energy consumption. Currently, there are limited NOx controls on fossil fuel combustion in China,30 leading to the much higher NOx emission factor in China than in the US and Europe.34 Therefore, although the per capita energy consumption in China was lower than that in the US and The Netherlands, the energy N footprint in China was larger (Figure 2d). Total National N Footprint and Future Perspective. The total national N footprint linearly increased from 19 to 42 Tg N yr−1 between 1980 and 2008 in China (Figure 2a). This suggests that environmental issues caused by anthropogenic Nr creation are a major challenge facing China in this century.35,36 Food Nr loss was the largest contributor to the N footprint in China. To meet the human food demand, the N input to China’s cropland has been far greater than its optimal fertilization rate,37 leading to losses of Nr as high as 21 Tg N yr−1. The amount of grain used as feed accounts for 60% of total grain production, prolonging the food chain of protein production that ultimately reduces the overall NUE of food production from 45 to 9% in 2008 (Figure S4, SI). This NUE reduction caused an extra loss of 10 Tg N yr−1, which increased the total Nr loss as a result of food production to 31 Tg N yr−1, which was greater than the total synthetic N fertilizer used for agricultural production in China.19 Currently, China is experiencing a rapid transformation of the composition of food consumption.32 The proportion of animal protein to total protein consumption in China increased from about 10% in 1980 to 33% in 2008; however, this proportion is still lower than the global average (39%) and much lower than that of developed countries (60−80%, ref 32). Through a correlation analysis with data from China’s provinces and 171 countries,32 we found that per capita foodrelated N consumption and the proportion of animal protein to total protein consumption significantly increased with socioeconomic development (per capita GDP and urbanization level, Figure 5). Therefore, given the socioeconomic development, the animal protein consumption in China would be at least doubled before 2050. This would result in an enormous pressure on food production and would continue to increase the food N footprint in China. Reducing the Nr loss during food production and increasing the Nr reuse between related subsystems can reduce the food-related N footprint and benefit the environment. For instance, utilizing the national “Fertilization According to Soil Test Result” (FASTR) program33 and

Figure 5. Relationships between per capita N consumption and socioeconomic development. (a) N consumption and PGDP; (b) N consumption and urbanization; (c) fraction of animal protein (meat and milk) to total N consumption and PGDP; (d) fraction of animal protein and urbanization. PGDP = per capita gross domestic product (constant 2000$). China is represented by the 31 provinces in China, not including Taiwan, Hong Kong, and Macao. Other countries represent the 171 countries that form the FAO (2013) statistic data set on food consumption.

increasing the FCR of livestock24,26 can reduce the N footprint related to food production. China’s per capita energy consumption is much lower than that in the developed countries;34 thus, the high NOx emission per unit energy consumed is the main reason for the high per capita energy N footprint in China. The total Nr released to the environment via energy use increased from 1.0 to 6.1 Tg N yr−1 from 1980 to 2008. Given the trends for future socioeconomic development, the projected energy N consumption is expected to increase and lead to more serious pollution to the atmosphere if no measures are taken to control NOx emissions from fossil fuel combustion, especially from coal burning.30 Similar problems exist with regards to the nonfood product N footprint, with the Nr release from this aspect also increasing rapidly from 1.5 to 6.7 Tg N yr−1 from 1980 to 2008; this trend is expected to continue with future socioeconomic development. However, most of the Nr lost from the consumption of nonfood products is lost to the environment slowly because it is contained in structural products. When the environmental effects occur is determined by the lifespan of the service and the degradation rate of the nonfood products.14 This time lag of Nr release from goods may result in even larger environmental effects in the future, which must be fully considered in making policies related to the total N footprint.

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ASSOCIATED CONTENT

S Supporting Information *

Detailed descriptions of the data and methods used for calculating the N balance in the CHANS and uncertainty analysis. Table S1, the hierarchical structure of the CHANS; Tables S2−S10, data sources and main parameters; Figures S1−S3, frameworks of N cycles among functional groups and subsystems and the method used to compile the data; Figure S4, yield, N input, and NUE in cropland and livestock. This 9222

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(14) Gu, B.; Yang, G.; Luo, W.; Du, Y.; Ge, Y.; Chang, J. Rapid growth of industrial nitrogen fluxes in China: Driving forces and consequences. Sci. China Earth Sci. 2013, 56 (4), 662−670. (15) Davidson, E.; David, M.; Galloway, J.; Goodale, C.; Haeuber, R.; Harrison, J.; Howarth, R.; Jaynes, D.; Lowrance, R.; Nolan, T.; Peel, J.; Pinder, R.; Porter, E.; Snyder, C.; Townsend, A.; Ward, M. H. Excess nitrogen in the U.S. environment: Trends, risks, and solutions. Issues Ecol. 2012, 15, 1−16. (16) Liu, J.; Dietz, T.; Carpenter, S. R.; Alberti, M.; Folke, C.; Moran, E.; Pell, A. N.; Deadman, P.; Kratz, T.; Lubchenco, J.; Ostrom, E.; Ouyang, Z.; Provencher, W.; Redman, C. L.; Schneider, S. H.; Taylor, W. W. Complexity of coupled human and natural systems. Science 2007, 317 (5844), 1513−1516. (17) Alberti, M.; Asbjornsen, H.; Baker, L. A.; Brozovic, N.; Drinkwater, L. E.; Drzyzga, S. A.; Jantz, C. A.; Fragoso, J.; Holland, D. S.; Kohler, T. A.; Liu, J.; McConnell, W. J.; Maschner, H. D. G.; Millington, J. D. A.; Monticino, M.; Podestá, G.; Pontius, R. G.; Redman, C. L.; Reo, N. J.; Sailor, D.; Urquhart, G. Research on coupled human and natural systems (CHANS): Approach, challenges, and strategies. Bull. Ecol. Soc. America 2011, 92 (2), 218−228. (18) Kennedy, C.; Pincetl, S.; Bunje, P. The study of urban metabolism and its applications to urban planning and design. Environ. Pollut. 2011, 159 (8), 1965−1973. (19) NBSC (National Bureau of Statistics of China). China Statistical Yearbook; NBSC Press: Beijing, China, 1981−2009. (20) Gu, B.; Chang, J.; Ge, Y.; Ge, H.; Yuan, C.; Peng, C.; Jiang, H. Anthropogenic modification of the nitrogen cycling within the Greater Hangzhou Area system, China. Ecol. Appl. 2009, 19 (4), 974−988. (21) Kaye, J. P.; Groffman, P. M.; Grimm, N. B.; Baker, L. A.; Pouyat, R. V. A distinct urban biogeochemistry? Trends Ecol. Evol. 2006, 21 (4), 192−199. (22) Hong, B.; Swaney, D. P.; Howarth, R. W. A toolbox for calculating net anthropogenic nitrogen inputs (NANI). Environ. Modell. Software 2011, 26 (5), 623−633. (23) Min, Y.; Gong, W.; Jin, X.; Chang, J.; Gu, B.; Han, Z.; Ge, Y. NCNA: Integrated platform for constructing, visualizing, analyzing and sharing human-mediated nitrogen biogeochemical networks. Environ. Modell. Software 2011, 26 (5), 678−679. (24) Smil, V. Nitrogen and food production: Proteins for human diets. AMBIO: J. Hum. Environ. 2002, 31 (2), 126−131. (25) Hou, F. J.; Nan, Z. B.; Xie, Y. Z.; Li, X. L.; Lin, H. L.; Ren, J. Z. Integrated crop-livestock production systems in China. Rangeland J. 2008, 30 (2), 221−231. (26) Oenema, O. Nitrogen budgets and losses in livestock systems. Int. Congr. Ser. 2006, 1293 (0), 262−271. (27) Li, P. Exponential growth, animal welfare, environmental and food safety impact: The case of China’s livestock production. J. Agric. Environ. Ethics 2009, 22 (3), 217−240. (28) Qian, Y. Appropriate process and technology for wastewater treatment and reclamation in China. Water Sci. Technol. 2000, 42, 107−114. (29) Qiu, Y.; Shi, H.-c.; He, M. Nitrogen and phosphorous removal in municipal wastewater treatment plants in China: A review. Int. J. Chem. Eng. 2010, DOI: 10.1155/2010/914159. (30) Gu, B.; Ge, Y.; Ren, Y.; Xu, B.; Luo, W.; Jiang, H.; Gu, B.; Chang, J. Atmospheric reactive nitrogen in China: Sources, recent trends, and damage costs. Environ. Sci. Technol. 2012, 46 (17), 9420− 9427. (31) Jakob, M.; Marschinski, R. Interpreting trade-related CO2 emission transfers. Nat. Climate Change 2012, 3 (1), 19−23. (32) FAO (Food and Agriculture Organization of the United Nations). FAOSTAT: FAO Statistical Databases. http://faostat.fao. org/default.aspx (Accessed Mar 20, 2013). (33) Gu, B.; Zhu, Y.; Chang, J.; Peng, C.; Liu, D.; Min, Y.; Luo, W.; Howarth, R. W.; Ge, Y. The role of technology and policy in mitigating regional nitrogen pollution. Environ. Res. Lett. 2011, 6 (1), 014011. (34) OECD (Organization for Economic Co-operation and Development). OECD environmental indicators, development,

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AUTHOR INFORMATION

Corresponding Author

*Tel/fax: +86 571 8820 6465; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported by the National Science Foundation of China (Grant Nos. 41201502 and 31170305) and China Postdoctoral Science Special Foundation (Grant No. 2012T50508).

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REFERENCES

(1) Galloway, J. N.; Townsend, A. R.; Erisman, J. W.; Bekunda, M.; Cai, Z.; Freney, J. R.; Martinelli, L. A.; Seitzinger, S. P.; Sutton, M. A. Transformation of the nitrogen cycle: Recent trends, questions, and potential solutions. Science 2008, 320 (5878), 889−892. (2) Canfield, D. E.; Glazer, A. N.; Falkowski, P. G. The evolution and future of earth’s nitrogen cycle. Science 2010, 330 (6001), 192−196. (3) Rockström, J.; Steffen, W.; Noone, K.; Persson, Å.; Chapin, F. S.; Lambin, E. F.; Lenton, T. M.; Scheffer, M.; Folke, C.; Schellnhuber, H. J. A safe operating space for humanity. Nature 2009, 461 (7263), 472− 475. (4) Wackernagel, M.; Onisto, L.; Bello, P.; Callejas Linares, A.; Susana López Falfán, I.; Méndez García, J.; Isabel Suárez Guerrero, A.; Guadalupe Suárez Guerrero, M. National natural capital accounting with the ecological footprint concept. Ecol. Economics 1999, 29 (3), 375−390. (5) Wackernagel, M.; Schulz, N. B.; Deumling, D.; Linares, A. C.; Jenkins, M.; Kapos, V.; Monfreda, C.; Loh, J.; Myers, N.; Norgaard, R. Tracking the ecological overshoot of the human economy. Proc. Natl. Acad. Sci. 2002, 99 (14), 9266−9271. (6) Dietz, T.; Rosa, E. A.; York, R. Driving the human ecological footprint. Front. Ecol. Environ. 2007, 5 (1), 13−18. (7) Wiedmann, T.; Minx, J. A definition of ‘carbon footprint’. In Ecological Economics Research Trends; Pertsova, C., Ed.; Nova Science: Hauppauge, NY, US, 2008; pp 1−11. (8) Galli, A.; Wiedmann, T.; Ercin, E.; Knoblauch, D.; Ewing, B.; Giljum, S. Integrating ecological, carbon and water footprint into a “footprint family” of indicators: Definition and role in tracking human pressure on the planet. Ecol. Indic. 2012, 16, 100−112. (9) Leach, A. M.; Galloway, J. N.; Bleeker, A.; Erisman, J. W.; Kohn, R.; Kitzes, J. A nitrogen footprint model to help consumers understand their role in nitrogen losses to the environment. Environ. Dev. 2012, 1 (1), 40−66. (10) Qin, S.; Hu, C.; Zhang, Y.; Wang, Y.; Dong, W.; Li, X. Advances in nitrogen footprint research. Chin. J. Eco-Agric. 2011, 19 (2), 462− 467. (11) Č uček, L.; Klemeš, J. J.; Kravanja, Z. A review of footprint analysis tools for monitoring impacts on sustainability. J. Cleaner Prod. 2012, 34 (0), 9−20. (12) Galloway, J. N.; Burke, M.; Bradford, G. E.; Naylor, R.; Falcon, W.; Chapagain, A. K.; Gaskell, J. C.; McCullough, E.; Mooney, H. A.; Oleson, K. L. L.; Steinfeld, H.; Wassenaar, T.; Smil, V. International trade in meat: The tip of the pork chop. AMBIO: J. Hum. Environ. 2007, 36 (8), 622−629. (13) Jensen, L. S.; Schjoerring, J. K.; van der Hoek, K. W.; Poulsen, H. D.; Zevenberge, J. F.; Pallière, C.; Lammel, J.; Brentrup, F.; Jongbloed, A. W.; Willems, J.; Grinsven, H. v. Benefits of nitrogen for food, fibre and industrial production. In The European Nitrogen Assessment; Sutton, M. A., Howard, C. M., Erisman, J. W., Billen, G., Bleeker, A., Grennfelt, P., Grinsven, H. v., Grizzetti, B., Eds.; Cambridge University Press: Cambridge, 2011; pp 32−61. 9223

dx.doi.org/10.1021/es401344h | Environ. Sci. Technol. 2013, 47, 9217−9224

Environmental Science & Technology

Article

measurement and use. http://www.oecd.org/env/ (Accessed Mar 20, 2013). (35) Cui, S.; Shi, Y.; Groffman, P. M.; Schlesinger, W. H.; Zhu, Y.-G. Centennial-scale analysis of the creation and fate of reactive nitrogen in China (1910−2010). Proc. Natl. Acad. Sci. 2013, 110 (6), 2052− 2057. (36) Fan, M.; Shen, J.; Yuan, L.; Jiang, R.; Chen, X.; Davies, W. J.; Zhang, F. Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China. J. Exp. Bot. 2011, 63 (1), 13−24. (37) Ju, X.-T.; Xing, G.-X.; Chen, X.-P.; Zhang, S.-L.; Zhang, L.-J.; Liu, X.-J.; Cui, Z.-L.; Yin, B.; Christie, P.; Zhu, Z.-L.; Zhang, F.-S. Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proc. Natl. Acad. Sci. 2009, 106 (9), 3041−3046.

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