Predicting Common Air Quality Index-The Case of Czech Microregions

This paper presents a design of models for common air quality index prediction using computational intelligence methods. In addition, the sets of input variables were optimized for each air pollutant prediction by genetic algorithms. Based on data measured by the three monitoring stations of Dukla, Rosice and Brnenska in the Czech Republic, the models were designed to predict air quality indices for each air pollutant separately and, consequently, to predict the common air quality index. Considering the root mean squared error, the results showed that the compositions of individual prediction models significantly outperform single prediction models of the common air quality index. The feature selection procedure indicates that the determinants of air quality indices were strongly locality specific. Therefore, the models can be applied to obtain more accurate one day ahead predictions of air quality indices. Here we show that the composition models achieve high prediction accuracy for maximum air quality indices (between 50.69 and 63.36%). The goal of the prediction by various methods was to compare the results of the prediction with the aim of various recommendations to micro-regional public administration management.

[1]  F. Murena,et al.  Measuring air quality over large urban areas: development and application of an air pollution index at the urban area of Naples , 2004 .

[2]  Ujjwal Kumar,et al.  ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO) , 2010 .

[3]  E. Mantilla,et al.  Daily, seasonal and monthly variations in ozone levels recorded at the Turia river basin in Valencia (Eastern Spain) , 2012, Environmental Science and Pollution Research.

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  David E. Booth,et al.  Analysis of Incomplete Multivariate Data , 2000, Technometrics.

[6]  Harri Niska,et al.  Methods for imputation of missing values in air quality data sets , 2004 .

[7]  Diana Domanska,et al.  Application of fuzzy time series models for forecasting pollution concentrations , 2012, Expert Syst. Appl..

[8]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[9]  Anikender Kumar,et al.  Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis , 2013, Pure and Applied Geophysics.

[10]  Gülsen Aydin Keskin,et al.  Predicting tropospheric ozone concentrations in different temporal scales by using multilayer perceptron models , 2011, Ecol. Informatics.

[11]  Poonam Goyal,et al.  Air Quality Prediction of PM10 through an Analytical Dispersion Model for Delhi , 2014 .

[12]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[13]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[14]  Shikha Gupta,et al.  Linear and nonlinear modeling approaches for urban air quality prediction. , 2012, The Science of the total environment.

[15]  Petr Hájek,et al.  Prediction of Air Quality Indices by Neural Networks and Fuzzy Inference Systems - The Case of Pardubice Microregion , 2013, EANN.

[16]  P. Farrugia,et al.  Comparative analysis of estimators for wind direction standard deviation , 2006 .

[17]  Fitz-Simons.  Guideline for reporting of daily air quality: Air Quality Index (AQI) , 1999 .

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

[19]  Sef van den Elshout,et al.  CAQI Common Air Quality Index--update with PM(2.5) and sensitivity analysis. , 2014, The Science of the total environment.

[20]  Petr Hájek,et al.  Ozone prediction on the basis of neural networks, support vector regression and methods with uncertainty , 2012, Ecol. Informatics.

[21]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[22]  Amit Kumar Gorai,et al.  Development of a Fuzzy Pattern Recognition Model for Air Quality Assessment of Howrah City , 2014 .

[23]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[24]  Yun Zeng,et al.  Progress in developing an ANN model for air pollution index forecast , 2004 .

[25]  M. Zunckel,et al.  A novel air pollution index based on the relative risk of daily mortality associated with short-term exposure to common air pollutants , 2007 .

[26]  Eugenijus Perednis,et al.  Investigation of the wind characteristics and prospects of wind power use in Lithuania , 2013 .

[27]  Mahad Baawain,et al.  Systematic Approach for the Prediction of Ground-Level Air Pollution (around an Industrial Port) Using an Artificial Neural Network , 2014 .

[28]  K. P. Moustris,et al.  3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece , 2010 .

[29]  R. J. Yamartino,et al.  A Comparison of Several `Single-Pass' Estimators of the Standard Deviation of Wind Direction. , 1984 .

[30]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[31]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[32]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[33]  B. Vijay Bhaskar,et al.  Atmospheric Particulate Pollutants and their Relationship with Meteorology in Ahmedabad , 2010 .

[34]  Predrag Hercog,et al.  Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations , 2009 .

[35]  Joseph Rynkiewicz,et al.  A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions , 2007, Environ. Model. Softw..

[36]  Gavin C. Cawley,et al.  Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki , 2003 .

[37]  G. Cesaroni,et al.  Assessing the public health impacts of urban air pollution in 25 European cities: results of the Aphekom project. , 2013, The Science of the total environment.

[38]  Lazaros S. Iliadis,et al.  Intelligent Agents Networks Employing Hybrid Reasoning: Application in Air Quality Monitoring and Improvement , 2009, EANN.

[39]  Gabriel Ibarra-Berastegi,et al.  Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area , 2006, Environ. Model. Softw..

[40]  Papiya Mandal,et al.  High seasonal variation of atmospheric C and particle concentrations in Delhi, India , 2014, Environmental Chemistry Letters.

[41]  M. Santamouris,et al.  Experimental investigation of air flow and temperature distribution in deep urban canyons for natural ventilation purposes , 2006 .

[42]  Wenjian Wang,et al.  Online prediction model based on support vector machine , 2008, Neurocomputing.

[43]  Goodarz Ahmadi,et al.  AEROSOL AND AIR QUALITY RESEARCH , 2012 .

[44]  Karine Léger,et al.  Comparing urban air quality in Europe in real time a review of existing air quality indices and the proposal of a common alternative. , 2008, Environment international.

[45]  Stanislaw Osowski,et al.  Forecasting of the daily meteorological pollution using wavelets and support vector machine , 2007, Eng. Appl. Artif. Intell..

[46]  Dezhi Sun,et al.  Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification , 2011 .

[47]  Ping-Feng Pai,et al.  Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms , 2011, Appl. Math. Comput..

[48]  Jeffrey R. Brook,et al.  Regional Transport and Urban Contributions to Fine Particle Concentrations in Southeastern Canada , 2002, Journal of the Air & Waste Management Association.

[49]  Gilles Foret,et al.  Combining deterministic and statistical approaches for PM10 forecasting in Europe , 2009 .

[50]  Andreas N. Skouloudis,et al.  “Air-quality indicators” for uniform indexing of atmospheric pollution over large metropolitan areas , 1999 .

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

[52]  Yang Zhang,et al.  Real-time air quality forecasting, part I: History, techniques, and current status , 2012 .

[53]  George Papadourakis,et al.  Investigation and Forecasting of the Common Air Quality Index in Thessaloniki, Greece , 2012, AIAI.