Predictive analytics of PM10 concentration levels using detailed traffic data

Abstract PM 10 particles impose significant risks to human health and the well-being of individuals in general. However, due to the complexity of the inner-correlations between influencing environmental factors, the holistic approach to predictive analytics of PM 10 concentration levels is a challenging task yet to be undertaken. We base this study on the rationale that a prediction model is suitable for making accurate estimations involving knowledge about the hidden interactions that govern them. In addition to the model’s precision, it is, therefore, beneficial to provide a model that is interpretable, as this can assist in the decision about how and which prevention actions to take. For this purpose, a Genetic Algorithm is proposed that carries out multiple regression analysis by searching for the optimal fictional definition of a prediction model. As such, the obtained model is human interpretable, where the preliminary analysis conducted within this study proved its compliance with the existing studies, while the model itself proved to be considerably more accurate than the present state-of-the-art.

[1]  Derya Birant.  Comparison of Decision Tree Algorithms for Predicting Potential Air Pollutant Emissions with Data Mining Models , 2011 .

[2]  Åke Sjödin,et al.  Metal emissions from road traffic and the influence of resuspension: results from two tunnel studies , 2002 .

[3]  Dimitrios Melas,et al.  Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-sized Mediterranean City , 2007 .

[4]  Taek Mu Kwon,et al.  Blind Deconvolution Processing of Loop Inductance Signals for Vehicle Reidentification , 2006 .

[5]  M. Maricq.  Chemical characterization of particulate emissions from diesel engines: A review , 2007 .

[6]  I Nyoman Budiantara,et al.  Multivariable Semiparametric Regression Model with Combined Estimator of Fourier Series and Kernel , 2017 .

[7]  Jin-Nan Wang,et al.  China tackles the health effects of air pollution , 2013, The Lancet.

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

[9]  P. Lenschow,et al.  Some ideas about the sources of PM10 , 2001 .

[10]  Artemio Sotomayor-Olmedo,et al.  Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach , 2013 .

[11]  Jean-Michel Poggi,et al.  PM10 forecasting using clusterwise regression , 2011 .

[12]  Kurt Straif,et al.  The carcinogenicity of outdoor air pollution. , 2013, The Lancet Oncology.

[13]  Jean-Michel Poggi,et al.  Three Non-Linear Statistical Methods for Analyzing PM10 Pollution in Rouen Area , 2014 .

[14]  Bert Brunekreef,et al.  Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project. , 2012, Environmental science & technology.

[15]  J. Chow,et al.  A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile , 2008 .

[16]  Frank de Leeuw,et al.  Air quality status and trends in Europe , 2014 .

[17]  Yong Huang,et al.  Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models , 2017 .

[18]  I. Barmpadimos,et al.  Influence of meteorology on PM 10 trends and variability in Switzerland from 1991 to 2008 , 2010 .

[19]  L. Duponchel,et al.  Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation , 2009 .

[20]  Siegfried Hörmann,et al.  Analysis and prediction of particulate matter PM10 for the winter season in Graz , 2016 .

[21]  Ka-Veng Yuen,et al.  Prediction of daily averaged PM10 concentrations by statistical time-varying model , 2009 .

[22]  Nicolas Moussiopoulos,et al.  PM10 forecasting for Thessaloniki, Greece , 2006, Environ. Model. Softw..

[23]  Petri Nokelainen,et al.  Investigating the Number of Non-linear and Multi-modal Relationships Between Observed Variables Measuring Growth-oriented Atmosphere , 2007 .

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

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

[26]  Joel Grus,et al.  Data Science from Scratch: First Principles with Python , 2015 .

[27]  Siegfried Hörmann,et al.  Quality and performance of a PM10 daily forecasting model , 2008 .

[28]  Yong Joo Park,et al.  Genotoxic effects and oxidative stress induced by organic extracts of particulate matter(PM 10)collected from a subway tunnel in Seoul, Korea. , 2012, Mutation research.

[29]  Domen Mongus,et al.  Predictive analytics of tree growth based on complex networks of tree competition , 2018, Forest Ecology and Management.

[30]  Domen Mongus,et al.  Two-level evolutionary algorithm for discovering relations between nodes' features in a complex network , 2017, Appl. Soft Comput..

[31]  F. J. Trujillo,et al.  Prediction of PM10 and SO2 exceedances to control air pollution in the Bay of Algeciras, Spain , 2014, Stochastic Environmental Research and Risk Assessment.

[32]  Gemmer Marco,et al.  Air Quality Legislation and Standards in the European Union: Background, Status and Public Participation , 2013 .

[33]  Mukesh Khare,et al.  ARTIFICIAL NEURAL NETWORK BASED LINE SOURCE MODELS FOR VEHICULAR EXHAUST EMISSION PREDICTIONS OF AN URBAN ROADWAY , 2004 .

[34]  Ligia Vizeu Barrozo,et al.  Air pollution: a potentially modifiable risk factor for lung cancer , 2013, Nature Reviews Cancer.

[35]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[36]  Hamid Taheri Shahraiyni,et al.  Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies , 2016 .

[37]  Jorge Reyes,et al.  Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago, Chile , 2002 .

[38]  Arwa S. Sayegh,et al.  Comparing the Performance of Statistical Models for Predicting PM10 Concentrations , 2014 .

[39]  Ana Isabel Miranda,et al.  Evaluating strategies to reduce urban air pollution , 2016 .

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

[41]  J. Balmes,et al.  Outdoor air pollution and asthma , 2014, The Lancet.