Developing a methodology to predict PM10urban concentrations using GLM

A methodology using Generalized Linear Models (GLM) was developed and tested to build a model to predict PM10 outdoor urban concentrations. The methodology is based in the previous study of the relations between atmospheric concentrations of air pollutants CO, NO2, NOx, VOCs, SO2, and meteorological variables, air temperature, relative humidity and wind speed, in a particular city (Barreiro, Portugal). The model uses data from the Portuguese monitoring air quality stations network, and meteorological data. The developed GLM model consider as dependent variable PM10 outside air concentrations, and considers as explanatory independent variables or covariates, the air concentrations of pollutants NO2, NOx, CO, O3 but also the meteorological variables, air temperature, relative humidity of outside air and wind speed. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was dedicated to cases with maximum air temperature below 25oC and maximum air temperature above 25oC. Results indicate that best performance results were achieved for model with values of maximum air temperature above 25oC, when compared with model considering all data, or when compared with model considering maximum air temperature below 25oC. The model was also tested with data from other Portuguese city (Oporto).

[1]  P. H. Anderson,et al.  7 Air Pollution and Climate Change , 2009 .

[2]  P. Gupta,et al.  Particulate Matter Air Quality Assessment using Integrated Surface, Satellite, and Meteorological Products , 2009 .

[3]  Francisco Ferreira,et al.  Lisbon air quality forecast using statistical methods , 2009 .

[4]  Chiara F. Ferraris,et al.  NIST Recommended Practice Guide: The Use of Nomenclature in Dispersion Science and Technology | NIST , 2001 .

[5]  Rex Britter,et al.  Dynamics and dispersion modelling of nanoparticles from road traffic in the urban atmospheric environment—A review , 2011 .

[6]  J. Schwartz,et al.  Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states. , 2012, Environmental science & technology.

[7]  Michael Frankfurter,et al.  Statistical Methods For Environmental Pollution Monitoring , 2016 .

[8]  Philip Demokritou,et al.  Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece , 2003 .

[9]  P. Saldiva,et al.  Modelos MLG e MAG para análise da associação entre poluição atmosférica e marcadores de morbi-mortalidade: uma introdução baseada em dados da cidade de São Paulo , 2001 .

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

[11]  A. Wellburn Air pollution and climate change , 1994 .

[12]  M. C. Hubbard,et al.  A Comparison of Nonlinear Regression and Neural Network Models for Ground-Level Ozone Forecasting , 2000, Journal of the Air & Waste Management Association.

[13]  J. Pekkanen,et al.  Prevalence of asthma symptoms in video and written questionnaires among children in four regions of Finland. , 1997, The European respiratory journal.

[14]  W. Geoffrey Cobourn,et al.  Accuracy and reliability of an automated air quality forecast system for ozone in seven Kentucky metropolitan areas , 2007 .

[15]  M. Demuzere,et al.  A new method to estimate air-quality levels using a synoptic-regression approach. Part I: Present-day O3 and PM10 analysis , 2010 .

[16]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[17]  M. Hashim,et al.  A robust calibration approach for PM 10 prediction from MODIS aerosol optical depth , 2012 .

[18]  Joan O. Grimalt,et al.  Prediction of daily ozone concentration maxima in the urban atmosphere , 2006 .

[19]  Diofantos G. Hadjimitsis,et al.  The development of air quality indices through image-retrieved AOT and PM10 measurements in Limassol Cyprus , 2012, Remote Sensing.

[20]  P. Gupta,et al.  Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach , 2009 .