The objective of this study was to investigate how a meaningful PMF solution can be obtained with smaller datasets using the features included in USEPA version 5. In all 29 PM10 and 33 PM2.5 samples were collected from a rural setup in Apulia. The filters were characterised chemically. Running the model with the PM10 and PM2.5 samples separately resulted in the model not working correctly. The datasets for the two PM fractions were therefore aggregated in a single dataset made up of 62 samples. PMF isolated 5 different factors. The solution returned by PMF was validated against the solutions returned by two other models CWNMF and CMB. The daily samples reconstructed by the three models were compared to each other using Pearson correlation and the source contribution estimates returned by the three models were also compared
Stability of a PMF derived source apportionment solution using a smaller dataset
A. Genga;T. Siciliano;
2020-01-01
Abstract
The objective of this study was to investigate how a meaningful PMF solution can be obtained with smaller datasets using the features included in USEPA version 5. In all 29 PM10 and 33 PM2.5 samples were collected from a rural setup in Apulia. The filters were characterised chemically. Running the model with the PM10 and PM2.5 samples separately resulted in the model not working correctly. The datasets for the two PM fractions were therefore aggregated in a single dataset made up of 62 samples. PMF isolated 5 different factors. The solution returned by PMF was validated against the solutions returned by two other models CWNMF and CMB. The daily samples reconstructed by the three models were compared to each other using Pearson correlation and the source contribution estimates returned by the three models were also comparedI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
