| With the development of social economy and the acceleration of urbanization and industrialization,the problem of pollutant metabolism in urban ecosystem is becoming more and more serious.At the same time,with the rapid increase of energy consumption and demand,the adjustment of industrial structure,and the resource allocation caused by urban expansion make it difficult to quantify and analyze pollutant emissions in urban ecosystems to some extent.How to quantify the multi-pollutant emissions under complex conditions,analyze the interaction between multi-level industries,identify the emission reduction responsibilities of various emitters and evaluate the emission reduction benefits,and optimize the industrial structure and pollutant metabolism of the management system?These problems will affect the healthy development of urban ecosystem and the improvement of the management level of urban pollutant system.On the basis of fully recognizing the characteristics and complexity of the urban pollutant metabolism system,this research aims to construct and promote the urban pollutant metabolism control system and its optimal management.By considering the constraints among society,economy and environment,a coupled multi-pollutant metabolism model is constructed,which quantitatively reflects the trade-off relationship among the three systems,evaluates the present situation of pollutant metabolism problems,and provides a multi-pollutant collaborative control and optimal management scheme on this basis.Specific contents will include:(1)Multi-perspective and multi-layer industrial pollutant emission base investigation and its dynamic evolution.In order to quantify the metabolic problems of multiple pollutants,a pollutant metabolic model based on input-output analysis is developed.The direct and indirect emissions of wastewater,COD wastewater,ammonia nitrogen wastewater,various heavy metals(Hg,As,Cr,Cd,Pb),SO2,NOx and dust are quantified from the perspectives of production,consumption and investment.The emission list of multiple pollutants in the whole industry is constructed,and the urgent pollutant emission problems in key industries are identified,which provided support for the subsequent formulation of pollutant emission-reduction policies.In order to analyze the dynamic change of various pollutants in long-time scale,a dynamic network deconstruction model based on structural decomposition analysis is developed,the emission pattern of multiple pollutants for many years is analyzed.The direct and indirect emission of various pollutants in each year is quantified.In addition,the pollutant metabolism network is dynamically deconstructed by using the emission pattern of pollutants for many years.The contribution degree of driving factors to each pollutant is identified and quantified,which provides support for the subsequent industrial structure upgrading.(2)Identification of emission responsibility and assessment of emission reduction effect under the influence of multi-regional interaction.Trade exchanges and interactions among different regions have aggravated the problem of pollutant metabolism to a certain extent,and the complicated interactions have made the determination of the main responsibility of emission unclear.Therefore,a multi-dimensional and two-level model of stepwise cluster is constructed.At the industry level,based on the multi-perspective accounting of pollutant emissions,the responsibility of emission reduction in the whole industry is quantified,and emission reduction policies with industry characteristics are formulated.At the regional level,the regional emission reduction policy is formulated by quantifying the pollutant emissions induced by multi-regional trade.At the same time,taking Guangdong Province as an example,this research analyzes the pollutant emissions caused by inter-provincial trade activities in Guangdong Province,quantifies the emissions caused by inter-provincial intermediate use,final demand and other economic activities,and quantifies the emission reduction responsibilities of different provinces and industries.In addition,a stepwised cluster model based on different income levels is constructed to analyze the differences of pollutant emissions caused by residents with different income levels nationwide.Finally,a multi-factor interaction model of environmental emission reduction policies based on factorial analysis is constructed to quantify the contribution of main and interaction effects of different factors to pollutant emission reduction,and evaluate various emission reduction policies by coupling social,economic and environmental factors.(3)System equilibrium optimization with multiple pollutant emission constraints.The extensive use of fossil energy has aggravated the emission of pollutants to a certain extent.Therefore,an input-output model based on energy optimization is constructed to plan the energy structure transformation in the future periods(2020-2060),simulate the energy transformation route that meets the goal of net-zero carbon,and couple the interval parametric programming to realize the energy planning with the lowest system cost and meeting the social,economic and environmental constraints.On this basis,the emission status of various pollutants after the optimization of energy structure is quantitatively analyzed,and the emission pattern of pollutants from 2030 to 2060 is deconstructed.Based on social,economic and environmental factors,this research explores the coordinated control and optimal management of multiple pollutants in urban ecosystem.At the same time,this research coupled with input-output analysis,stepwised cluster analysis,ecological network analysis,factorial analysis,energy system optimization and other analysis methods,realized the quantitative research on the metabolism of multiple pollutants,compiled the pollutant discharge list of the whole industry,analyzed the dynamic emission pattern of multiple pollutants,identified and evaluated the responsibility of emission reduction,and optimized the energy structure to promote the collaborative management of pollutants.The results of this research will contribute to the formulation of relevant urban policies and improve the decision-making efficiency. |
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