A system dynamics model for managing regional N inputs from human activities
Introduction
Nitrogen (N), and especially its reactive forms (Nr) have long been recognized as a critical factor in controlling primary production in terrestrial, marine and aquatic ecosystems of the biosphere (Gruber and Galloway, 2008). The N fluxes between living organisms and physical environment, also referred to as the N cycle, are one of the most fundamental nutrient cycles on Earth. Before the invention of Haber–Bosch process in the early 1900s, N inputs to terrestrial ecosystem were dominated by natural sources, mainly from biological N fixation (Galloway et al., 2004). However, by the mid-1970s, anthropogenic N input sources had grown to match these natural N input sources and have exceeded them since then (Howarth, 2004). A recent research article showed that the anthropogenic perturbation of the N cycle exceeded the “safe operating boundary” of the Earth by a factor of 3.5 (Rockstrom et al., 2009). The enrichment of N in the environment and its adverse effects on human health, water quality and biodiversity have been extensively reported and have become increasingly important issues (Cui et al., 2013, Erisman et al., 2013, Howarth et al., 2000, Smith and Schindler, 2009, Townsend et al., 2003, Vitousek et al., 1997).
Globally, food production and energy production are the two main drivers of anthropogenic N inputs (Galloway et al., 2004). In the modern food production system, synthetic fertilizer application is the main source of N inputs to crop systems which ultimately improve and increase crop and livestock production. Currently, synthetic fertilizer is the largest source of Nr from human actions, and its quantity and relative contribution are expected to further increase in the future (Galloway et al., 2004). In addition to crop production, animal production is a substantial driver for N inputs (Bouwman et al., 2013). Reducing the proportion of meat in diet, especially ruminant meat, has been considered an important approach to mitigate N pollution (Kastner et al., 2012, Lassaletta et al., 2013, Ma et al., 2013).
In the energy production system, Nr can be released as a product of fossil fuel combustion in which the high temperature of combustion breaks the N2 bonds of atmospheric nitrogen. In some developed areas such as the northeastern US, NOy deposition resulting from energy production is a very significant contribution, making up 29% net anthropogenic N inputs (Hong et al., 2011). When considering relatively small spatial scales such as a country or a catchment, trade patterns may be a dominant factor shaping regional N cycles (Gao et al., 2014a, Lassaletta et al., 2013). Because of its many chemical forms and the variability of its sources, quantification and management of N have been a great challenge.
To quantify the human influence on N inputs in a region, Howarth et al. (1996) introduced a quasi-mass-balance method, the net anthropogenic N inputs (NANI), to estimate human-induced N inputs to a specific region. In the NANI calculations, only those processes that bring new N to the region are considered N inputs, simplifying the calculation and allowing flexibility in the data required. The NANI approach has been widely applied to many watersheds of different sizes and climatic conditions (Boyer et al., 2006, Chen et al., 2014, Howarth et al., 1996, Swaney et al., 2012), demonstrating that it is an effective tool for characterizing human-induced N inputs to the landscape and their impacts on riverine N export (Hong et al., 2013). Riverine N exports account for 21–25% NANI in 106 U.S. watersheds (Hong et al., 2013) and the percentage varies depending on land use patterns and climate (Howarth et al., 2012, Zhang et al., 2014). However, its applications so far have been mainly limited to examining historic trends or assessing current conditions. Since N sources are expected to be altered substantially in the foreseeable future (Han and Allan, 2012), it is important to be able to project changes in NANI and its drivers, especially in response to changes in policy, providing useful information for decision making.
Based on the NANI concept, in this study we developed a regional model for calculating NANI using a system dynamics (SD) approach, hereafter referred to as NANI-SD. The system dynamics model approach was first developed by Forrester in 1958 for use in industrial management (Forrester, 1958). The SD model is preferred when large, dynamic and complex simulations are needed, which is very common in both environmental and socioeconomic systems. Because of its excellent capacity for simulating feedback relationships within and among systems especially when the socioeconomic system is involved, the SD model is widely used in studies of environmental carrying capacity, water resources and water environment management (Liu et al., 2015). In this study, the model establishes a connection between the anthropogenic drivers and NANI, allowing the users to simulate alterations and interactions of N production, N consumption, N trade, and NANI calculation in a region of interest. We demonstrate the use of this system dynamics approach to examine key drivers of regional alteration of N inputs, such as population, diet choice and trade patterns, evaluated under different policy scenarios. We used Lake Dianchi basin in China as a case study in which NANI has been recently estimated (Gao et al., 2014b). Lake Dianchi basin currently suffers from severe N pollution, and developing tools for predicting the potential future of NANI in response to different environmental policies and identifying key variables controlling NANI have a high priority in this region.
Section snippets
Case study area
Lake Dianchi is the largest freshwater lake in the Yunnan-Guizhou Plateau and the sixth largest in China, and has been given the name of “Pearl of the Plateau”. The basin is located in central Kunming City, the capital of Yunnan province in southwestern China (24°29–25°28′ N, 102°29–103°01′ E; Fig. 1), covering a total area of 2920 km2 with average altitude of 1900 m. Land cover in the basin is dominated by forest (47.3% in 2008), followed by cropland (19.9%), urban (16.5%), water (10.8%), and
Current anthropogenic N inputs to the Lake Dianchi basin
In 2010, net anthropogenic N input to the Lake Dianchi basin was 11,800 kg-N/km2/yr, which is much higher than most regions of China and the world (Han et al., 2014, Zhang et al., 2014). Synthetic fertilizer and food import are the most important contributions, accounting for 63% and 33%, respectively. The other two components of NANI, agricultural N fixation and N deposition, are negligible in this region, making up less than 5% of the total NANI. Crop and flower productions are the main
Tradeoff between N self-sufficiency and N pollution
NANI is closely related to local food production, which can alter N inputs to a region through fertilization, agricultural N fixation, and food and feed imports. Since N self-sufficiency is dependent on local food production and consumption, it should also be a factor directly influencing NANI. However, results from the two scenarios relevant to local food production (S3 and S4 in Fig. 8) indicate that the response of NANI to food self-sufficiency is complex. To gain further insights into how N
Conclusion
Based on a system dynamics approach and the net anthropogenic N input (NANI) concept, a NANI-SD model was developed to simulate the relationship between NANI and its drivers. A case study in China using the NANI-SD model suggests that regional N inputs are controlled by many interactive factors, and the performance of a certain policy generally combines the net effects from multiple influences of the policy, as successfully captured by our model. Overlooking the systematic linkage of the
Acknowledgement
This work was supported by the Major Science and Technology Program for Water Pollution Control and Treatment of China (2013ZX07102).
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