Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality

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• 作者：Jianjun He ; Ye Yu ; Yaochen Xie ; Hongjun Mao ; Lin Wu ; Na Liu…
• 刊名：Water, Air, and Soil Pollution
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：227
• 期：7
• 全文大小：1,871 KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Environment
  Atmospheric Protection, Air Quality Control and Air Pollution
  Waste Water Technology, Water Pollution Control, Water Management and Aquatic Pollution
  Terrestrial Pollution
  Hydrogeology
  
• 出版者：Springer Netherlands
• ISSN：1573-2932
• 卷排序：227

文摘

Knowledge of the relationship between air quality and impact factors is very important for air pollution control and urban environment management. Relationships between winter air pollutant concentrations and local meteorological parameters, synoptic-scale circulations and precipitation were investigated based on observed pollutant concentrations, high-resolution meteorological data from the Weather Research and Forecast model and gridded reanalysis data. Artificial neural network (ANN) model was developed using a combination of numerical model derived meteorological variables and variables indicating emission and circulation type variations for estimating daily SO₂, NO₂, and PM₁₀ concentrations over urban Lanzhou, Northwestern China. Results indicated that the developed ANN model can satisfactorily reproduce the pollution level and their day-to-day variations, with correlation coefficients between the modeled and the observed daily SO₂, NO₂, and PM₁₀ ranging from 0.71 to 0.83. The effect of four factors, i.e., synoptic-scale circulation type, local meteorological condition, pollutant emission variation, and wet removal process, on the day-to-day variations of SO₂, NO₂, and PM₁₀ was quantified for winters of 2002–2007. Overall, local meteorological condition is the main factor causing the day-to-day variations of pollutant concentrations, followed by synoptic-scale circulation type, emission variation, and wet removal process. With limited data, this work provides a simple and effective method to identify the main factors causing air pollution, which could be widely used in other urban areas and regions for urban planning or air quality management purposes.KeywordsAir qualitySynoptic-scale circulationLocal meteorologyPollutant emissionRemoval processANN