Source Apportionment of PM2.5 in Milan by PMF Receptor Model
Source identification of air pollution
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This case study discusses the application of a multivariate receptor model, the EPA PMF 5.0 to the PM2.5 dataset from Lombardy region in Italy. The aim of the study is to perform source apportionment investigation of the applied dataset and identify different PM2.5 sources that greatly impact the composition of particulate matter in the studied region. PMF model evaluates contribution to diverse source types of measured PM2.5 concentrations by investigating chemical composition of ambient pollution samples. As a type of receptor models, PMF used as an input data, PM concentrations and their re...
This case study discusses the application of a multivariate receptor model, the EPA PMF 5.0 to the PM2.5 dataset from Lombardy region in Italy. The aim of the study is to perform source apportionment investigation of the applied dataset and identify different PM2.5 sources that greatly impact the composition of particulate matter in the studied region. PMF model evaluates contribution to diverse source types of measured PM2.5 concentrations by investigating chemical composition of ambient pollution samples. As a type of receptor models, PMF used as an input data, PM concentrations and their relative chemical specification and provides as an outcome the number of sources, their composition and the source contributions. The analysis of total annual PM2.5 mass concentration revealed presence of 6 sources (secondary sulfate, traffic non-exhaust, biomass combustion/break wear, domestic heating, re-suspended soil dust and secondary nitrate).
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