Systems Ecological Analysis Of The Biogeochemical Metabolism Of The Urban-rural Complexes

The impacts of human on ecosystems have rised from modification to domination in a world of increasing urbanization. Human alterations to the ecosystems have improved human welfare to a great extent, while at the same time causing a cascade of environmental problems, and threating the health of human and ecosystems. At present, more than half of the world’s population lives in urban areas and this ratio is expected to reach 70% by 2050, thus the relationship between biogeochemical metabolism of urban area and human well-being will become more closely. Urban greenspace is the system that is most closely related to human’s daily life, it can provide ecosystem services to human while also has negative effects, such as the emissions of biogenic volatile organic compounds (BVOC), which could cause haze weather and ozone pollution in urban area. Another system in urbanized area that has close relationship with human well-being is agricultural production system. Crop cultivation and livestock breeding provide food to human. They are also the primary sources of atmospheric emissions of ammonia and greenhouse gases, which are the main causes of regional atmospheric haze formation and may influence climate change at global scale.Urban-rural complex is composed by built-up area and the surrounding rural areas. Its biogeochemical processes are dominated by both social-economic factors and natural environmental factors. Only by using systems ecological methods can we reveal the characteristics of the complex metabolic system. However, existing systems ecological models have some shortcomings when applied to urban studies:on the one hand, most of the models are based on the research of natural systems, and lack of integration of human factors when conducting systematic analysis; on the other hand, existing biogeochemical researches mainly base on the use of single-element mass balance models, thus cannot characterize the couplings among different element cycles. The above two aspects are constraints for conducting comprehensive analysis and regulation on the metabolism of the urban-rural complex.In this paper, new systems ecological models are built to overcome the above problems. The model mainly includes a system dynamics sub-model to integrate human factors and natural factors and a flux balance analysis sub-model that allows multi-element analysis. Using these models, urban greenspace system inside built-up area and the livestock system outside the built-up area were chose as the cases to conduct systems ecological analysis and regulation. The main research contents are:on the basis of the experiment (urban vegetation survey, emission rate measurement), using the system dynamics model of urban BVOC emission to analyze the spatial and temporal patterns of BVOC emissions in Hangzhou area and to explore the role of human and natural factors in determining BVOC emissions; by analogy with the cell metabolic reconstruction, conducting five elements (carbon, hydrogen, oxygen, nitrogen and phosphorus) biogeochemical metabolic reconstruction of the milk production system; based on the metabolic reonstruction, conducting multi-element analysis and optimization for the metabolic process of milk production system in Shanghai by using the flow balance analysis model (FBA). The major conclusions of this paper are as follows:(1) In 2010, the total BVOC emissions from urban greenspace in Hangzhou were 0.47 Gg C (95CI:0.31-0.67 Gg C). Isoprene, monoterpene and other VOCs (OVOC) contributed 71.5%,20.2% and 8.3%, respectively. The emissions intensity (emissions per unit land area) of urban greenspace (3.1 Mg C km-2 yr-1) was higher than the average emissions intensity (2.4 Mg C km-2 yr-1) of rural forest in this region. The emission intensity of block green space is 3.9 Mg C km-2 yr-1, which was higher than that of line greenspace (2.6 Mg C km-2 yr-1). When comparing Hangzhou with two other areas located on the eastern coastline of China, it was found that the BVOC emission intensity in the three regions increased with a drop in latitude.(2) Among the primary species in Hangzhou, Salix babylonica (1.21 kg C tree-1 yr-1), Liquidambar formosana (0.80 kg C tree-1 yr-1), Albizia julibrissin (0.45 kg C tree-1 yr-1) and Platanus acerifolia (0.35 kg C tree-1 yr-1) are the tree species with highest individual emission potentials. There were no significant differences (p>0.05) in BVOC emission patterns between native and exotic species within Hangzhou, which is inconsistent with the previous couclusion that exotic species have higher BVOC emssions than native species. The reason may be that many exotic ornamental trees in the temperate zone origins from subtropical zone, whereas the exotic ornamental trees in Hangzhou (subtropical region) are also introduced from other subtropical regions, suggesting that the exotic and native species in subtropical region may have closer taxonomic relationships. Only four species, Cinnamomum camphora, P. acerifolia, S. babylonica and L. formosana contributed more than 75% of total BVOC emissions from urban green space in Hangzhou.(3) Modelling results showed that, BVOC emissions from urban greenspace of Hangzhou undergo a significant diurnal variation. Isoprene emissions showed a sharp peak and only occurred during daytime hours, with a maximum value reached in 14:00 pm. Monoterpenes and OVOC emissions had slight diurnal variations and emitted during day and night. There were also considerable differences in BVOC emissions during different seasons. The largest emission occurs in summer, accounting for 67% of total emissions in the whole year of 2010; in the meantime, the least emission occurs in winter, only accounting for less than 2% of the year. The diurnal and seasonal variations of BVOC emissions are primarily determined by the short-term (temperature and PAR) and long-term (phenology) impact of the environmental factors.(4) In all environmental changes scenarios (global warming, heat island effect, PAR change and CO2 concentration increase) and human management scenarios (greenspace expansion, regulating the tree species composition of existing and newly planted trees, tree density change), total urban BVOC emissions showed rapidly rising trends, increasing from 0.47 Gg C in 2010 to 1.2-3.2 Gg C in 2050, with a growing rate of 155%-580%, much higher than that of emissions from rural forest. This means that urban greenspaces have become important VOC emission sources, and will have greater impacts on regional air quality. Compared with the baseline scenario (only tree growth is considered), human management factors have a bigger impact on the change amplitude of BVOC emission in the future (-32%=70%) than that of environmental factors (-12%-30%), indicating that human management factors will play a more important role in determining future urban BVOC emissions. It also highlights the importance of bringing human factors into the system model. Greenspace management strategies have a time-lag effect on BVOC emissions, meaning that the difference in BVOC emissions between different strategies is small for the first few years but increases over time, this requires managers to be more forward-looking when making policies.(5) The paper proposed the leaf biomass/BVOC emissions ratio to roughly represent the benefit to urban ecosystem services. Modelling results showed that a highest ecosystem service value (leaf mass/BVOC/ratio is 39.9) could be achieved through positive coping in confronting environmental changes and adopting proactive urban management strategies (moderately increase tree density while restricting excessive greenspace expansion and optimizing the species composition of existing and newly planted trees); the negative response to environmental changes and reckless urban development (urban green space sprawl, inappropriate selection of tree species) will greatly limit the net ecosystem services provided by greenspace (leaf mass/BVOC/ ratio is 20.8).(6) Through ecosystem metabolic reconstruction, the biogeochemical metabolism of the whole milk production system can be expressed as 277 metabolic reactions participated by 191 metabolites. Representing metabolites and reaction controllers within the system as nodes and connecting them based on their mutual transformative relationships, we obtained a metabolic network of the milk production system. The network consists of 468 nodes (191 are metabolites, the rest are reaction controllers) and 1165 edges. The nodes have an average degree of 4.98. The node degree distribution is in accordance with the power law distribution, so the network is also a scale-free network, just like the internet and social networks. The diameter of the network is 16 and the average length of shortest paths is 5.8, indication the network has a "small world" feature.(7) The milk production system in Shanghai contributed to meet the demand of human nutrition, while at the same time caused damege cost to environmental health and human health. Based on metabolic reconstruction and life cycle impact assessment, to produce 1 kg milk in Shanghai will emit 629.5 kg CO2,43.7 kg CH4,15.7 kg CO, 4.1kg NOx,1.8 kg N2O,1.0 kg NH3,3.3 kg PM and 0.6 VOC kg to air, discharge 16.0 kg nitrogen and 2.0 kg phosphorus to water, and consume 47.6 ton water,109.0 kg primary energy (in standard coal), and 0.7 kg mineral phosphate. The total damage cost was 3500 yuan ton-1 milk, of which human health damage accounts for 45%, damage to ecosystem health accounts for 52%, and damage to the resources accounts for 3%,(8) In milk production system, the optimization of carbon cycle have trade-offs to both the optimization of nitrogen cycle and phosphorus cycle, while the optimization of nitrogen cycle and phosphorus cycle have synergies. When set carbon element as the optimization goal, the environmental impacts associated with carbon dropped by 23.2%, however at this time, the environmental impact associated with the nitrogen cycle and phosphorus cycle has worsened, the damage cost associated with nitrogen and phosphorus increased by 12.3% and 12.3%, respectively; When set nitrogen as the optimization goal, the related environmental impact of nitrogen fell by 25.8%, at the same time environmental impact associated with phosphorus cycle improved by 18.2%, the environmental impact associated with the carbon cycle has worsened, its environmental costs increased by 19.2%. The reason for the trade-off between the optimizations of carbon and nitrogen is that many strategies to control nitrogen discharge to air and water will consume a lot of fossil fuels, and this will increase carbon-related emissions, such as CO2, CO and VOC. The measures to mitigate nitrogen discharges to water can also reduce phosphorus discharge, that is why the optimization of nitrogen cycle and phosphorus cycle has synergies.(9) Of the 78 possible pairs of environmental emissions or resource consumption categories,33 pairs were significantly correlated (p< 0.05). Among them, phosphorus discharge to water and VOC emissions, and nitrogen discharge to to water and VOC emissions have the biggest trade-off intensity -0.81 (R value); CO2 emissions and VOC emissions has the strongest synergy, synergy intensity is 0.97.(10) The multi-element analysis and optimization can be realized through metabolic reconstruction, which can weaken tradeoff and utilize synergy, thus achieving more comprehensive management and optimization. When considering the comprehensive environmental impacts and setting the total damage cost as the optimization objective, the environmental cost of milk production system decreases by 22.4%, and the environmental impacts are reduced to different degrees. And in a single element optimization, total costs only reduce by 3.7% to 17.4%. In the actual management, decision makers can choose the items according to the actual need to optimize the objective and set the weight to achieve a comprehensive optimization, which is impossible through single element optimization.