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

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.

[1]  P Hyde,et al.  Forecasting PM10 in metropolitan areas: Efficacy of neural networks. , 2012, Environmental pollution.

[2]  Hamza Abderrahim,et al.  Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks , 2015, Environmental science and pollution research international.

[3]  N. T. Kim Oanh,et al.  Meteorological pattern classification and application for forecasting air pollution episode potential in a mountain-valley area , 2005 .

[4]  William C. Skamarock,et al.  A time-split nonhydrostatic atmospheric model for weather research and forecasting applications , 2008, J. Comput. Phys..

[5]  Monica Campbell,et al.  A Synoptic Climatological Approach to Assess Climatic Impact on Air Quality in South-central Canada. Part I: Historical Analysis , 2007 .

[6]  O. Jorba,et al.  Influence of high-model grid resolution on photochemical modelling in very complex terrains , 2005 .

[7]  Matt Riley,et al.  Summarising climate and air quality (ozone) data on self-organising maps: a Sydney case study , 2016, Environmental Monitoring and Assessment.

[8]  Georgios Grivas,et al.  Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece , 2006 .

[9]  Y. Q. Wang,et al.  Spatial distribution and interannual variation of surface PM 10 concentrations over eighty-six Chinese cities , 2010 .

[10]  Rob J Hyndman,et al.  Quantifying the influence of local meteorology on air quality using generalized additive models , 2011 .

[11]  A. Gertler,et al.  Trans-boundary transport of ozone from the Eastern Mediterranean Coast , 2011 .

[12]  Surajit Chattopadhyay,et al.  Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone , 2007 .

[13]  Jianjun He,et al.  Below-cloud scavenging of aerosol particles by precipitation in a typical valley city, northwestern China , 2015 .

[14]  H. Auld,et al.  A Synoptic Climatological Approach to Assess Climatic Impact on Air Quality in South-central Canada. Part II: Future Estimates , 2007 .

[15]  Jianjun He,et al.  Integrated modeling of urban-scale pollutant transport: application in a semi-arid urban valley, Northwestern China , 2013 .

[16]  C. Hogrefe,et al.  A comparison between 2010 and 2006 air quality and meteorological conditions, and emissions and boundary conditions used in simulations of the AQMEII-2 North American domain , 2015 .

[17]  He Jian-ju Impact of Land Surface Information on WRF's Performance in Complex Terrain Area , 2014 .

[18]  Yuan Cheng,et al.  Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions , 2015 .

[19]  Na Liu,et al.  Numerical model-based relationship between meteorological conditions and air quality and its implication for urban air quality management , 2013 .

[20]  C. Lijuan,et al.  Atmospheric Environmental Capacity of SO2 in Winter over Lanzhou in China: A Case Study , 2007 .

[21]  Li Li,et al.  APLICATION OF BP NEURAL NETWORK TO FORECASTING API IN BEIJING , 2011 .

[22]  Tong Zhu,et al.  The impact of circulation patterns on regional transport pathways and air quality over Beijing and its surroundings , 2011 .

[23]  J. Hooyberghs,et al.  A neural network forecast for daily average PM10 concentrations in Belgium , 2005 .

[24]  Kim N. Dirks,et al.  Effects of local, synoptic and large‐scale climate conditions on daily nitrogen dioxide concentrations in Auckland, New Zealand , 2014 .

[25]  Cameron C. Lee,et al.  Utilizing map pattern classification and surface weather typing to relate climate to the Air Quality Index in Cleveland, Ohio , 2012 .

[26]  Lin Wu,et al.  Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality , 2016 .

[27]  Rob J. Hyndman,et al.  Investigating the influence of synoptic-scale meteorology on air quality using self-organizing maps and generalized additive modelling , 2011 .

[28]  A. Piazzalunga,et al.  High secondary aerosol contribution to particulate pollution during haze events in China , 2014, Nature.

[29]  Source attribution of particulate matter pollution over North China with the adjoint method , 2015 .

[30]  C. Willmott ON THE VALIDATION OF MODELS , 1981 .

[31]  Patricia Rosales,et al.  Comparison between the , 2010 .

[32]  Radan Huth,et al.  AN INTERCOMPARISON OF COMPUTER‐ASSISTED CIRCULATION CLASSIFICATION METHODS , 1996 .

[33]  Yafeng Yin,et al.  Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach , 2009 .

[34]  I. Kioutsioukis,et al.  High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation , 2016 .