Carvalho, Maria da Graça

Cerdeira, Rita

Coelho, Luís

Garcia, João

Teodoro, F.

English

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A METHODOLOGY TO ESTIMATE PM10 OUTDOOR URBAN CONCENTRATIONS USING GLM   J.M. Garcia (1), F. Teodoro (1), R. Cerdeira (1), L.M.R. Coelho (1), M.G. Carvalho (2)(3)   (1) Escola Superior de Tecnologia de Setubal, Instituto Politécnico de Setúbal, Campus do IPS, 2910-761 Setúbal, Portugal (2) Instituto Superior Técnico, Portugal, (3) European Parliament Brussels, Belgium Presenting author email: joao.garcia@estsetubal.ips.pt    Summary This study presents a methodology that uses Generalized Linear Model (GLM) to predict PM10 concentrations based in the previous study of the relations between PM10 air concentrations and CO, NO2, NOx, VOCs, SO2 atmospheric concentrations, but also meteorological variables as air temperature, relative humidity and wind speed. The study is applied to a particular city (Barreiro, Portugal) and the model uses data from the monitoring air quality stations Portuguese network, and meteorological data. The developed GLM consider as dependent variable (or response variable) PM10 outside air concentrations, and considers as explanatory independent variables or covariates the air concentrations f pollutants NO2, NOx, CO, O3 but also meteorological variables, air temperature, relative humidity and wind speed. A logarithmic lnk function was considered with a Poisson probability d stribution.  The studied model includes detailed inspection for cases with maximum air temperature below 25ºC and maximum air temperature above 25ºC. Results indicate that best performance is achieved for model considering only data with maxi um air temperature values above 25ºC (R2=0,649) when compared with model considering all data (R2=0,386) and also when compared with model that considerers only data with maximum air temperature below 25ºC (R2=0,149). The best performance model was tested withdata from other Portuguese city (Oporto) showing reasonable fit results.  Introduction In past recent, the concern about air quality has growth mainly due to the increasing knowledge of relations between respiratory problems, especially in children’s and ir pollution. In the case of PM, is known that airborne particulate matter has its origin in a wide variety of natural and anthropogenic sources. Some particles are directly emitt d nto the atmosphere and others may be formed in the air as a result of the chemical reaction of gases. Articles dedicated to study the contribution to air pollution from different sources, conclude that there is a strong relation between gaseous air pollutants and atmospheric PM.  Methodology and Results General Linear Model (GLM) (Nelder et Wedderburn, 1972) was used to building a methodology to estimate PM10 outside concentrations, based on known values of other outside gaseous air pollutant concentrations. GLM are based in the assumption that there are K independent values Y1, ..., YK, from a variable of interest or response variable (effect) that follows an exponential family distribution with expected value E (Yi) = µi [Conceição et al, 2001]. Considering K vectors xi = (1 xi1 x12 … xip)t, i=1, ..., K, containing the values of p explanatory variables, independent or covariates. A link logarithmic function was considered and Yi has a Poisson distribution, the model results in a Poisson regression model and each term βi is the effect of variable Xi in g (µi). In fact, βi represents the “effect” of variable Xi in the function g(µi). In this case the objective is to estimate PM10 concentration values based on other variables, which are gaseous air pollutant concentrations namely CO, NO2, NOx, O3 and SO2 but also meteorological variables namely temperature (T,ºC), relative humidity (RH,%) and wind velocity (WV, m/s). Statistical Package software for Social Sciences SPSS 10.0 for windows was used to build and analyse the model.  Conclusions Results show that model considering all data predicts, poorly results for PM10 concentrations (R2=0,386) but when two sub models were developed, considering only data with the criteria of maximum temperature of air i) above 25ºC  and ii) below 25ºC results improved substantially.  Comparisons of the three models show that best performance results are achieved for model with values of Tmax air>25ºC (R2=0,649) showing the importance of air temperature in the formation of the secondary particles in air.  Acknowledgement The authors wish to acknowledge Comissão de Coordenação e Desenvolvimento Regional de Lisboa e Vale do Tej (CCDR-LVT) and Instituto de Metereologia (IM) by the information provided.  References Nelder J.A., Wedderburn R.W.M. (1972). “Generalized linear models”. J R Stat Soc A; 35: pg 370-84.  Conceição  G.M.S, Saldiva P. H. N.,Singer J. M., (2001). “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”. Rev. Bras. Epidemiol. 206 Vol. 4, Nº 3, pp 206-219. 

A methodology to Estimate PM10 Outdoor Urban Concentrations Using GLM.

https://comum.rcaap.pt/bitstream/10400.26/22688/1/JG.1.10.pdf

A methodology to Estimate PM10 Outdoor Urban Concentrations Using GLM.

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