Source apportionment of organic compounds in Berlin using positive matrix factorization — Assessing the impact of biogenic aerosol and biomass burning on urban particulate matter

2012 ◽  
Vol 435-436 ◽  
pp. 392-401 ◽  
Author(s):  
Sandra Wagener ◽  
Marcel Langner ◽  
Ute Hansen ◽  
Heinz-Jörn Moriske ◽  
Wilfried R. Endlicher
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Organic Compounds ◽  
Biomass Burning ◽  
Matrix Factorization ◽  
Positive Matrix Factorization ◽  
Positive Matrix ◽  
Urban Particulate Matter ◽  
The Impact

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.

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

scholarly journals VOC source apportionment and emission inventory evaluation over the great Athens, comparison with other cities of the Mediterranean basin

2019 ◽  
Author(s):  
Αναστασία Πανοπούλου
Keyword(s):  
Volatile Organic Compounds ◽  
Source Apportionment ◽  
Organic Compounds ◽  
Matrix Factorization ◽  
Mediterranean Basin ◽  
Emission Inventory ◽  
Positive Matrix Factorization ◽  
Positive Matrix ◽  
Volatile Organic ◽  
The Mediterranean Basin

Παρά τις πρωτοβουλίες για τον περιορισμό και μείωση των ατμοσφαιρικών ρύπων, στη Μεσόγειο και τις πόλεις που την περιβάλλουν εξακολουθούν να καταγράφονται υπερβάσεις των προβλεπόμενων οριακών τιμών για την προστασία της υγείας. Παρόλα αυτά, οι μελέτες στα αστικά κέντρα της περιοχής για Πτητικούς Οργανικούς Υδρογονάνθρακες (Volatile Organic Compounds ή VOC), οι οποίοι είναι πρόδρομοι του τροποσφαιρικού όζοντος (Ο3) και των αερολυμάτων, είναι περιορισμένες, ενώ ορισμένες υποδεικνύουν αβεβαιότητες στις παρατηρήσεις που προκύπτουν από συγκρίσεις με βάσεις δεδομένων εκπομπών, σχετιζόμενες με τη συνεισφορά των πηγών εκπομπής και το χημικό τους αποτύπωμα. Συνεπώς, η Αθήνα αποτελεί ιδανική τοποθεσία για μετρήσεις των ενώσεων αυτών, λόγω της μη-καταγραφής των επιπέδων τους τα τελευταία 15 χρόνια (πλην ελάχιστων εξαιρέσεων), της συνεχούς υπέρβασης των ορίων του Ο3 και των αερολυμάτων, καθώς και της αύξησης των εκπομπών από μέχρι πρότινος ασθενείς πηγές ρύπων (π.χ. καύση ξύλου για οικιακή θέρμανση). Στην παρούσα εργασία παρουσιάζονται τα αποτελέσματα μιας 17μηνης καμπάνιας ατμοσφαιρικών μετρήσεων πεδίου για μη-Μεθανικούς Υδρογονάνθρακες (non-Methane Hydrocarbons ή NMHCs) στην Αθήνα (Οκτώβριος 2015 - Φεβρουάριος 2017), στο πλαίσιο του διεθνούς προγράμματος ChArMEX (The Chemistry - Aerosol Mediterranean Experiment). Παράλληλα, εκπονήθηκαν δύο εντατικές περίοδοι εποχικών μετρήσεων (χειμώνα και καλοκαίρι) στον ίδιο σταθμό και επιπλέον, δύο εκστρατείες συλλογής δειγμάτων αέρα σε γνωστές πηγές ρύπανσης (σήραγγα και αστικός σταθμός μετρήσεων).Τα δεδομένα περισσότερων από 40 VOC με 2 έως 16 άτομα άνθρακα, που συλλέχθηκαν κατά τη διάρκεια της καμπάνιας, χρησιμοποιήθηκαν για τη μελέτη της ημερήσιας και εποχιακής διακύμανσης τους σε ετήσια βάση και των παραγόντων που την επηρεάζουν, ενώ τα επίπεδα C2 - C3 NMHCs στην Αθήνα παρουσιάζονται για πρώτη φορά. Η εποχικότητα παρουσιάζει σαφή διακύμανση, με μέγιστο το χειμώνα και ελάχιστο το καλοκαίρι για την πλειονότητα των ενώσεων, ενώ η ημερήσια διακύμανση επηρεάζεται από την ένταση των εκπομπών των πηγών, την ταχύτητα του ανέμου και το ύψος του στρώματος ανάμειξης. Η σύγκριση των αποτελεσμάτων αυτών με παρόμοιες έρευνες σε άλλες πόλεις ανέδειξαν το ρόλο των πηγών στα παρατηρούμενα επίπεδα, όπου για την Αθήνα αυτή η επίδραση είναι πιο έντονη τον χειμώνα. Επιπρόσθετα, τα μονοτερπένια και το ισοπρένιο, γνωστές ενώσεις βιογενούς προέλευσης, παρουσίασαν μία μοναδική μεταβλητότητα επηρεασμένη από ανθρωπογενείς εκπομπές, η οποία δεν λαμβάνεται υπόψη κατά την εκτίμηση της ποιότητας του αέρα. Τέλος, η χρήση του στατιστικού μοντέλου Positive Matrix Factorization (PMF) επέτρεψε τον προσδιορισμό των κύριων πηγών NMHCs στην Αθήνα και την εκτίμηση της συνεισφοράς τους στα επίπεδα των συγκεντρώσεων. Από αυτές, οι εκπομπές από την κίνηση οχημάτων και την οικιακή θέρμανση επικρατούν, ενώ μια δεύτερη PMF προσομοίωση στα δεδομένα της εποχικής εντατικής περιόδου παρατήρησης επιβεβαίωσε τα αποτελέσματα, δίνοντας επίσης πληροφορίες για πρόσθετες πηγές.

scholarly journals Source apportionment of PM<sub>10</sub> in a North-Western Europe regional urban background site (Lens, France) using Positive Matrix Factorization and including primary biogenic emissions

2013 ◽  
Vol 13 (10) ◽  
pp. 25325-25385 ◽  
Author(s):  
A. Waked ◽  
O. Favez ◽  
L. Y. Alleman ◽  
C. Piot ◽  
J.-E. Petit ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Biomass Burning ◽  
Matrix Factorization ◽  
Iteration Process ◽  
Positive Matrix Factorization ◽  
Biogenic Emissions ◽  
Positive Matrix ◽  
Urban Background ◽  
Background Site ◽  
Urban Background Site

Abstract. In this work, the source of ambient particulate matter (PM10) collected over a one year period at an urban background site in Lens (France) were determined and investigated using a~Positive Matrix Factorization receptor model (US EPA PMF v3.0). In addition, a Potential Source Contribution Function (PSCF) was performed by means of the Hysplit v4.9 model to assess prevailing geographical origins of the identified sources. A selective iteration process was followed for the qualification of the more robust and meaningful PMF solution. Components measured and used in the PMF include inorganic and organic species: soluble ionic species, trace elements, elemental carbon (EC), sugars alcohols, sugar anhydride, and organic carbon (OC). The mean PM10 concentration measured from March 2011 to March 2012 was about 21 μg m−3 with typically OM, nitrate and sulfate contributing to most of the mass and accounting respectively for 5.8, 4.5 and 2.3 μg m−3 on a yearly basis. Accordingly, PMF outputs showed that the main emission sources were (in a decreasing order of contribution): secondary inorganic aerosols (28% of the total PM10 mass), aged marine emissions (19%), with probably predominant contribution of shipping activities, biomass burning (13%), mineral dust (13%), primary biogenic emissions (9%), fresh sea salts (8%), primary traffic emissions (6%) and heavy oil combustion (4%). Significant temporal variations were observed for most of the identified sources. In particular, biomass burning emissions were negligible in summer but responsible for about 25% of total PM10 and 50% of total OC at wintertime. Conversely, primary biogenic emissions were found to be negligible in winter but to represent about 20% of total PM10 and 40% of total OC in summer. The latter result calls for more investigations of primary biogenic aerosols using source apportionment studies, which quite usually disregards this type of sources. This study furthermore underlines the major influence of secondary processes during daily threshold exceedances. Finally, apparent discrepancies that could be generally observed between filter-based studies (such as the present one) and Aerosol Mass Spectrometer-based PMF analyses (organic fractions) are also discussed here.

scholarly journals Source Apportionment of Particulate Matter in Urban Snowpack Using End-Member Mixing Analysis and Positive Matrix Factorization Model

2021 ◽  
Vol 13 (24) ◽  
pp. 13584
Author(s):  
Mikhail Y. Semenov ◽  
Natalya A. Onishchuk ◽  
Olga G. Netsvetaeva ◽  
Tamara V. Khodzher
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Matrix Factorization ◽  
Anthropogenic Activities ◽  
Positive Matrix Factorization ◽  
Point Sources ◽  
Aluminum Production ◽  
Positive Matrix ◽  
Factorization Model ◽  
First Time

The aim of this study was to identify particulate matter (PM) sources and to evaluate their contributions to PM in the snowpack of three East Siberian cities. That was the first time when the PM accumulated in the snowpack during the winter was used as the object for source apportionment study in urban environment. The use of long-term integrated PM samples allowed to exclude the influence of short-term weather conditions and anthropogenic activities on PM chemistry. To ascertain the real number of PM sources and their contributions to air pollution the results of source apportionment using positive matrix factorization model (PMF) were for the first time compared to the results obtained using end-member mixing analysis (EMMA). It was found that Si, Fe and Ca were the tracers of aluminosilicates, non-exhaust traffic emissions and concrete deterioration respectively. Aluminum was found to be the tracer of both fossil fuel combustion and aluminum production. The results obtained using EMMA were in good agreement with those obtained using PMF. However, in some cases, the non-point sources identified using PMF were the combinations of two single non-point sources identified using EMMA, whereas the non-point sources identified using EMMA were split by PMF into two single non-point sources. The point sources were clearly identified using both techniques.

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