Developing A Methodology To Predict PM10 Urban 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...

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Bibliographic Details
Published in:WIT Transactions on Ecology and the Environment 2014-01, Vol.183, p.49
Main Authors: Garcia, J M, Teodoro, F, Cerdeira, R, Coelho, L M R, Carvalho, M G
Format: Article
Language:English
Subjects:
Air monitoring
Air pollution
Air quality
Air quality assessments
Air temperature
Atmospheric models
Dependent variables
Environmental monitoring
Generalized linear models
Humidity
Independent variables
Methodology
Model testing
Nitrogen dioxide
Particulate matter
Poisson distribution
Pollutants
Probability distribution
Relative humidity
Statistical models
Sulfur dioxide
Temperature effects
Variables
Wind speed
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Summary: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 25[degrees]C and maximum air temperature above 25[degrees]C. Results indicate that best performance results were achieved for model with values of maximum air temperature above 25[degrees]C, when compared with model considering all data, or when compared with model considering maximum air temperature below 25[degrees]C. The model was also tested with data from other Portuguese city (Oporto).
ISSN:1746-448X
1743-3541
DOI:10.2495/AIR140051