Assessment of PM2.5 sources and their corresponding level of uncertainty in a coastal urban area using EPA PMF 5.0 enhanced diagnostics

Abstract

24Datasets that include only the PM elemental composition and no other important constituents such as 25 ions and OC, should be treated carefully when used for source apportionment. This work is 26 demonstrating how a source apportionment study utilizing PMF 5.0 enhanced diagnostic tools can 27 achieve an improved solution with documented levels of uncertainty for such a dataset.The uncertainty 28 of the solution is rarely reported in source apportionment studies or it is reported partially. Reporting 29 the uncertainty of the solution is very important especially in the case of small datasets. PM2.5 samples 30 collected in Patras during the year 2011 were used. The concentrations of 22 elements (Z=11-33) were 31 determined using PIXE. Source apportionment analysis revealed that PM2.5 emission sources were 32 biomass burning (11%), sea salt (8%), shipping emissions (10%), vehicle emissions (33%), mineral dust 33 (2%) and secondary sulfates (33%) while unaccounted mass was 3%. Although Patras city center is 34 located in a very close proximity to the city's harbor, the contribution of shipping originating emissions 35 was never before quantified. As rotational stability is hard to be achieved when a small dataset is used 36 the rotational stability of the solution was thoroughly evaluated. A number of constraints were applied 37 to the solution in order to reduce rotational ambiguity. 38 39

Quantitative Assessment of PM2.5 Sources and Their Seasonal Variation in Krakow

“…As mentioned before, the S/N ratio is a basic criterion of classification; however, final decision depends on factorization results and ability to tell sources apart. After factorization run, PMF software gives possibility to analyze the factorization stability by the “Fpeak Bootstrap Method” (PMF 5.0 Help; Manousakas et al 2017). …”

“…Based on chemical profiles at the receptor site, the method provides the relative contributions of different pollution sources. Recently, a large number of papers considering source identification and apportionment has appeared (Viana et al 2008; Karagulian et al 2015; Amato et al 2016; Manousakas et al 2015, 2017). The previous paper (Samek et al 2017) contains application of PMF modeling for source identification and apportionment.…”

Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input

“…When it comes to the prediction of local aerosol concentrations as part of the urban roughness layer two main aspects need to be considered: sources of particles and characteristics of particle dispersion [30]. Motor traffic emissions regarding both the amount of combustion processes and blown up dust as well as tire and break abrasions are identified to be the major source of particles near urban arterials [4,6,38]. Vehicular emissions are related to the volume of traffic, vehicle type and speed [30], which, in turn, are assumed to be attributable to traffic sound.…”

The Use of Principal Component Analysis for Source Identification of PM2.5 from Selected Urban and Regional Background Sites in Poland

“…Commonly used methods of source apportionment are receptor models (RMs), which use the chemical speciation of aerosols collected at the receptor to infer the contribution of the possible sources to the ambient concentration of particulate matter and their chemical composition [7]. Several receptor models have been devised and widely used, such as chemical mass balance [17], positive matrix factorization [18] and principal component analysis [17,19]. Taking into account the above considerations, the aim of the following study was to analysed concentrations and chemical compositions of PM2.5 samples from 3 different measurement sites in Poland.…”