Scenario Analysis In The Application Of The Urban Air Pollutant Total Amount Forecast

With the rapid development of our country’s economy and the accelerating of urbanization, making the already fragile environment short face more pressure, especially the urban air pollutant emissions. To solve these environmental problems fundamentally, we have to shift from concentration control management to total emissions control, besides predict the pollutant total amount is the effective implementation of environmental management measures to realize the important means of total amount control, and this will fundamentally to avoid, reduce, control the generation and development of environmental issuesCity is an open, dynamic and complex systems development influenced by many factors, full of uncertainty, and the future exists the unexpected possibilities of development, so if we simply use the past development model of things to infer future development model has its limits, and it would be very difficult to make a precise prediction.The article expounds the differences and advantages between the scenario analysis method and traditional method, the classification of the scenario analysis method, steps and application scope, etc. In addition to analysis of the applicability of the scenario analysis of the total urban air pollutant emission projections, and scenario analysis of the total urban air pollutants Prediction step.This paper analyzes the process of research on total urban air pollutant emission scenarios, and identifies factors driving force, to identify key uncertainties, develop and set reasonable scenarios that describe and analyze the scene. Taking Xi’an as an example, it introduces the application scenario analysis of the total emissions of air pollutants in the city, and screened for the economy, energy policy and three important factor, considering the government’s enforcement and implementation efforts, the use of scenario analysis were set five scenarios for 2020 air pollutant emissions were predicted in Xi’an. Scenario one is trend extrapolation mode. This model continues the base year of the industrial structure model, and its development is completely dependent on the expansion of the scale of investment and resource consumption, emissions intensity remained at the original level. Economic development for the next 3.3 times the base year, the pollutant emissions of the base year is also about 3.3 times, so this mode is the phase-out mode, undesirable. Scenario two and scenario three are energy structure adjustment mode. These two modes are increased the amount of natural gas from 7.36% to 25% and 30% respectively, increase in proportion to the amount of vehicle energy consumption fell and coal supply, this mode is optimized mode. Scenario four and scenario five are emission factor adjustment mode. These two modes on the basis of Scenario two and Scenario three on emission factors were adjusted. Adjustment is mainly to enhance the desulfurization and denitrification removal and more stringent vehicle emission standards.The results showed that: scene one mode is the phase-out mode. SO2 and NOx emissions of this mode compared to the base year have significantly increased in multiples. SO2 emissions in scenarios two to five are all below the base year and emissions of SO2 under the four scenarios in turn decreases. NOX emissions in scenarios two to five are all higher than the base year, but the emissions of NOX under the four scenarios gradually decrease. Coal is the main source of SO2 emissions. In addition to the main source of NOX emissions under scenarios three from transport, the main source of other three models NOX emissions are all from coal.Scenario analysis compared to traditional forecasting method is more suitable for predicting the total urban air pollutant emissions. Predictions make policymakers in dealing with urban development and the environment in the future, more comprehensive and consciously thinking of taking an early method possible future environmental issues and environmental management and planning. Scenario-based planning and planning guidance to optimize decisions and avoid making mistakes, to provide decision support for the city’s economic development, environmental sustainability and rational allocation of resources, sustainable development for the city guide.