A review of the main strategies used in the interpretation of similar chemical profiles yielded by receptor models in the source apportionment of particulate matter

Chemosphere ◽  
2020 ◽  
pp. 128746
Author(s):  
Elson Silva Galvão ◽  
Rita de Cassia Feroni ◽  
Marcos Tadeu D’Azeredo Orlando
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Receptor Models ◽  
Similar Chemical ◽  
Chemical Profiles

scholarly journals Sources of ultrafine particles and chemical species along a traffic corridor: comparison of the results from two receptor models

2013 ◽  
Vol 10 (1) ◽  
pp. 54 ◽  
Author(s):  
Adrian J. Friend ◽  
Godwin A. Ayoko ◽  
Daniel Jager ◽  
Megan Wust ◽  
E. Rohan Jayaratne ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Chemical Composition ◽  
Human Health ◽  
Size Distribution ◽  
Source Apportionment ◽  
Chemical Species ◽  
Principal Component ◽  
Composition Analysis ◽  
Receptor Models

Environmental context Identifying the sources responsible for air pollution is crucial for reducing the effect of the pollutants on human health. The sources of the pollutants were found here by applying two mathematical models to data consisting of particle size distribution and chemical composition data. The identified sources could be used as the basis for controlling or reducing emissions of air pollution into the atmosphere. Abstract Particulate matter is common in our environment and has been linked to human health problems particularly in the ultrafine size range. In this investigation, the sources of particles measured at two sites in Brisbane, Australia, were identified by analysing particle number size distribution data, chemical species concentrations and meteorological data with two source apportionment models. The source apportionment results obtained by positive matrix factorisation (PMF) and principal component analysis–absolute principal component scores (PCA–APCS) were compared with information from the gaseous chemical composition analysis. Although PCA–APCS resolved more sources, the results of the PMF analysis appear to be more reliable. Six common sources were identified by both methods and these include: traffic 1, traffic 2, local traffic, biomass burning and two unassigned factors. Thus motor vehicle related activities had the greatest effect on the data with the average contribution from nearly all sources to the measured concentrations being higher during peak traffic hours and weekdays. Further analyses incorporated the meteorological measurements into the PMF results to determine the direction of the sources relative to the measurement sites, and this indicated that traffic on the nearby road and intersection was responsible for most of the factors. The described methodology that utilised a combination of three types of data related to particulate matter to determine the sources and combination of two receptor models could assist future development of particle emission control and reduction strategies.

scholarly journals Disparities in particulate matter (PM<sub>10</sub>) origins and oxidative potential at a city scale (Grenoble, France) – Part 1: Source apportionment at three neighbouring sites

2021 ◽  
Vol 21 (7) ◽  
pp. 5415-5437
Author(s):  
Lucille Joanna S. Borlaza ◽  
Samuël Weber ◽  
Gaëlle Uzu ◽  
Véronique Jacob ◽  
Trishalee Cañete ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Temporal Trends ◽  
Chemical Species ◽  
Fine Scale ◽  
Strong Correlations ◽  
Oxidative Potential ◽  
Pmf Model ◽  
Enhanced Resolution ◽  
Similar Chemical ◽  
Chemical Profiles

Abstract. A fine-scale source apportionment of PM10 was conducted in three different urban sites (background, hyper-center, and peri-urban) within 15 km of the city in Grenoble, France using Positive Matrix Factorization (PMF 5.0) on measured chemical species from collected filters (24 h) from February 2017 to March 2018. To improve the PMF solution, several new organic tracers (3-MBTCA, pinic acid, phthalic acid, MSA, and cellulose) were additionally used in order to identify sources that are commonly unresolved by classic PMF methodologies. An 11-factor solution was obtained in all sites, including commonly identified sources from primary traffic (13 %), nitrate-rich (17 %), sulfate-rich (17 %), industrial (1 %), biomass burning (22 %), aged sea salt (4 %), sea/road salt (3 %), and mineral dust (7 %), and the newly found sources from primary biogenic (4 %), secondary biogenic oxidation (10 %), and MSA-rich (3 %). Generally, the chemical species exhibiting similar temporal trends and strong correlations showed uniformly distributed emission sources in the Grenoble basin. The improved PMF model was able to obtain and differentiate chemical profiles of specific sources even at high proximity of receptor locations, confirming its applicability in a fine-scale resolution. In order to test the similarities between the PMF-resolved sources, the Pearson distance and standardized identity distance (PD-SID) of the factors in each site were compared. The PD-SID metric determined whether a given source is homogeneous (i.e., with similar chemical profiles) or heterogeneous over the three sites, thereby allowing better discrimination of localized characteristics of specific sources. Overall, the addition of the new tracers allowed the identification of substantial sources (especially in the SOA fraction) that would not have been identified or possibly mixed with other factors, resulting in an enhanced resolution and sound source profile of urban air quality at a city scale.

Source apportionment of particulate matter in China

2013 ◽  
Author(s):  
Baoqing Wang ◽  
Deqing Wang ◽  
Qitao Ma ◽  
Shuai Yin ◽  
Shu Yao
Keyword(s):  
Particulate Matter ◽  
Source Apportionment

scholarly journals Organic aerosol source apportionment by offline-AMS over a full year in Marseille

2017 ◽  
Author(s):  
Carlo Bozzetti ◽  
Imad El Haddad ◽  
Dalia Salameh ◽  
Kaspar Rudolf Daellenbach ◽  
Paola Fermo ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Mediterranean Sea ◽  
Source Apportionment ◽  
Organic Aerosol ◽  
Aerodynamic Diameter ◽  
Aerosol Mass Spectrometer ◽  
Aerosol Mass ◽  
Aerosol Source

Abstract. We investigated the seasonal trends of OA sources affecting the air quality of Marseille (France) which is the largest harbor of the Mediterranean Sea. This was achieved by measurements of nebulized filter extracts using an aerosol mass spectrometer (offline-AMS). PM2.5 (particulate matter with an aerodynamic diameter

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