Source apportionment with uncertainty estimates of fine particulate matter in Ostrava, Czech Republic using Positive Matrix Factorization

2016 ◽  
Vol 7 (3) ◽  
pp. 503-512 ◽  
Author(s):  
Teri Vossler ◽  
Libor Černikovský ◽  
Jiří Novák ◽  
Ronald Williams
Keyword(s):  
Czech Republic ◽  
Particulate Matter ◽  
Source Apportionment ◽  
Matrix Factorization ◽  
Fine Particulate Matter ◽  
Positive Matrix Factorization ◽  
Positive Matrix ◽  
Fine Particulate ◽  
Uncertainty Estimates

Source apportionment of fine particulate matter by positive matrix factorization in the metropolitan area of São Paulo, Brazil

2018 ◽  
Vol 202 ◽  
pp. 253-263 ◽  
Author(s):  
Regina Maura de Miranda ◽  
Maria de Fatima Andrade ◽  
Flavia Noronha Dutra Ribeiro ◽  
Kelliton José Mendonça Francisco ◽  
Pedro José Pérez-Martínez
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Metropolitan Area ◽  
Matrix Factorization ◽  
Fine Particulate Matter ◽  
Positive Matrix Factorization ◽  
Sao Paulo ◽  
São Paulo ◽  
Positive Matrix ◽  
Fine Particulate

Source Apportionment of Fine Particulate Matter in Phoenix, AZ, Using Positive Matrix Factorization

2007 ◽  
Vol 57 (6) ◽  
pp. 741-752 ◽  
Author(s):  
Steven G. Brown ◽  
Anna Frankel ◽  
Sean M. Raffuse ◽  
Paul T. Roberts ◽  
Hilary R. Hafner ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Matrix Factorization ◽  
Fine Particulate Matter ◽  
Positive Matrix Factorization ◽  
Positive Matrix ◽  
Fine Particulate

Source apportionment of SPM by positive matrix factorization and PM2.5 analysis in an urban industrial area

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pallavi Pradeep Khobragade ◽  
Ajay Vikram Ahirwar
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Matrix Factorization ◽  
Global Climate Model ◽  
Steel Production ◽  
Positive Matrix Factorization ◽  
Back Trajectory ◽  
Sampling Location ◽  
Positive Matrix ◽  
Content Type

Purpose The purpose of this study is to monitor suspended particulate matter (SPM), PM2.5 and source apportionment study for the identification of possible sources during the year 2018–2019 at Raipur, India. Design/methodology/approach Source apportionment study was performed using a multivariate receptor model, positive matrix factorization (PMFv5.0) with a view to identify the various possible sources of particulate matter in the area. Back-trajectory analysis was also performed using NOAA-HYSPLIT model to understand the origin and trans-boundary movement of air mass over the sampling location. Findings Daily average SPM and PM2.5 aerosols mass concentration was found to be 377.19 ± 157.24 µg/m³ and 126.39 ± 37.77 µg/m³ respectively. SPM and PM2.5 mass concentrations showed distinct seasonal cycle; SPM – (Winter ; 377.19 ±157.25 µg/m?) > (Summer; 283.57 ±93.18 µg/m?) > (Monsoon; 33.20 ±16.32 µg/m?) and PM2.5 – (Winter; 126.39±37.77 µg/m³) > (Summer; 75.92±12.28 µg/m³). Source apportionment model (PMF) have been applied and identified five major sources contributing the pollution; steel production and industry (68%), vehicular and re-suspended road dust (10.1%), heavy oil combustion (10.1%), tire wear and brake wear/abrasion (8%) and crustal/Earth crust (3.7%). Industrial activities have been identified as major contributing factor for air quality degradation in the region. Practical implications Chemical characterization of aerosols and identification of possible sources will be helpful in abatement of pollution and framing mitigating strategies. It will also help in standardization of global climate model. Originality/value The findings provide valuable results to be considered for controlling air pollution in the region.

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