A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction

Abstract Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi’an and Jinan are PM10 and PM2.5 respectively, and that the air quality of Xi’an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. HighlightsA new air quality monitoring and early warning system is proposed in this paper.Fuzzy comprehensive evaluation is introduced to determine the main pollutants and evaluate the degree of air pollution.A new hybrid model is proposed to improve forecasting performance of air pollutant concentration.The results are validated well in two cites in China.

[1]  J. Samet,et al.  Air Pollution and Cardiovascular Disease: A Statement for Healthcare Professionals From the Expert Panel on Population and Prevention Science of the American Heart Association , 2004, Circulation.

[2]  Kristen M. Foley,et al.  A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5 , 2011 .

[3]  Masud Yunesian,et al.  A novel, fuzzy-based air quality index (FAQI) for air quality assessment , 2011 .

[4]  Jeffrey M. Vukovich,et al.  Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: Multiple-year assessment and sensitivity studies , 2014 .

[5]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[6]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[7]  Feng Liu,et al.  Interval forecasts of a novelty hybrid model for wind speeds , 2015 .

[8]  Jianzhou Wang,et al.  A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting , 2015 .

[9]  Luis Pastor Sánchez Fernández,et al.  Air quality assessment using a weighted Fuzzy Inference System , 2016, Ecol. Informatics.

[10]  Whei-Min Lin,et al.  An enhanced radial basis function network for short-term electricity price forecasting , 2010 .

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

[12]  X. Tie,et al.  Contribution of regional transport to the black carbon aerosol during winter haze period in Beijing , 2016 .

[13]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[14]  Xin-She Yang,et al.  Design optimization of truss structures using cuckoo search algorithm , 2013 .

[15]  Alma Hodzic,et al.  A model inter-comparison study focussing on episodes with elevated PM10 concentrations , 2008 .

[16]  Anastasia K Paschalidou,et al.  Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management , 2011, Environmental science and pollution research international.

[17]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[18]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[19]  Lazim Abdullah,et al.  Classification of air quality using fuzzy synthetic multiplication , 2012, Environmental Monitoring and Assessment.

[20]  Qi Li,et al.  Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation , 2015 .

[21]  Weidong Zhang,et al.  Prediction of 24-hour-average PM(2.5) concentrations using a hidden Markov model with different emission distributions in Northern California. , 2013, The Science of the total environment.

[22]  Ciesin Global metrics for the environment: The Environmental Performance Index ranks countries‘ performance on high-priority environmental issues , 2016 .

[23]  Pavlos A Kassomenos,et al.  Development of an aggregate Air Quality Index for an urban Mediterranean agglomeration: relation to potential health effects. , 2007, Environment international.

[24]  P. J. García Nieto,et al.  Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain) , 2011, Math. Comput. Model..

[25]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[26]  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 .

[27]  Ruifeng Li,et al.  The establishment and application of fuzzy comprehensive model with weight based on entropy technology for air quality assessment , 2010 .

[28]  Beibei Sun,et al.  Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models , 2014 .

[29]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[30]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[31]  Jing Zhao,et al.  Techniques of applying wavelet de-noising into a combined model for short-term load forecasting , 2014 .

[32]  Jianzhou Wang,et al.  A hybrid model based on data preprocessing for electrical power forecasting , 2015 .

[33]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[34]  Feng Liu,et al.  The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region , 2015 .

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

[36]  Petr Hájek,et al.  Air pollution assessment using hierarchical fuzzy inference systems , 2009 .

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

[38]  Gökay Akkaya,et al.  An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing , 2015, Expert Syst. Appl..

[39]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[40]  Whei-Min Lin,et al.  A New Elman Neural Network-Based Control Algorithm for Adjustable-Pitch Variable-Speed Wind-Energy Conversion Systems , 2011, IEEE Transactions on Power Electronics.

[41]  T. J. Lyons,et al.  Comparison of the Revised Air Quality Index with the PSI and AQI indices. , 2007, The Science of the total environment.

[42]  Po-Tang Chen,et al.  An Air Quality Monitoring System for Urban Areas Based on the Technology of Wireless Sensor Networks , 2012 .

[43]  Xin-She Yang,et al.  Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan , 2014, Appl. Soft Comput..

[44]  Tharwat E. Alhanafy,et al.  Neuro Fuzzy Modeling Scheme for the Prediction of Air Pollution , 2010 .

[45]  Francis Tuluri,et al.  A GIS Based Approach for Assessing the Association between Air Pollution and Asthma in New York State, USA , 2014, International journal of environmental research and public health.

[46]  Wei Sun,et al.  Prediction of ozone levels using a Hidden Markov Model (HMM) with Gamma distribution , 2012 .

[47]  W. Geoffrey Cobourn,et al.  An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations , 2010 .

[48]  Ling Tang,et al.  A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting , 2015, Int. J. Inf. Technol. Decis. Mak..