Application of Positive Matrix Factorization for atmospheric aerosols sources identification in Sao Paulo city

This work is part of a comprehensive project conducted by Atmospheric Sciences Department and Medical School from the University of Sao Paulo that aims to evaluate the health and economic impact of fine particulate matter (PM2.5) in six Brazilian cities (Sao Paulo, Rio de Janeiro, Belo Horizonte, Recife, Curitiba and Porto Alegre).

The focus of this study was Sao Paulo Mega-city. Sao Paulo is situated in the Metropolitan Region of Sao Paulo (MRSP) house of 11 millions inhabitants it holds a lot of polluter industries and a 7 million car's fleet. These features are responsible for strong air quality degradation and a complex mixture of aerosols and gases in the atmosphere.

The contribution of this work was the identification of the emission source profile for the fine particles atmospheric concentration and its characterization by concentration and speciation. For this, it was collected daily 24 hours samples, during a year, starting in August 2007. The particles were characterized for its mass concentration, elemental speciation by means of X-Ray fluorescence analysis, ionic composition and Black Carbon mass concentration.

For the source identification (using the data obtained from these analyses described before) it was used an advanced algorithm in receptor model: Positive Matrix Factorization (PMF). One advantage in using this tool instead of the Principal Component Analysis (PCA) is that the first considers only non-negative contributions of the sources identified and considers the standard deviation of the measurements as loads for the inclusion of the variable in the analysis.

The major conclusions regarding the contribution of identified sources for the air quality shows a high vehicles contribution, mainly from the heavy-duty fleet, more associated to the incomplete combustion of diesel.