scholarly journals Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)

2016 ◽  
Author(s):  
M. H. Sowlat ◽  
S. Hasheminassab ◽  
C. Sioutas
Keyword(s):  
Los Angeles ◽  
Matrix Factorization ◽  
Particle Number ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
The Other ◽  
Auxiliary Variables ◽  
Positive Matrix ◽  
Urban Background ◽  
Secondary Aerosol

Abstract. In this study, the Positive Matrix Factorization (PMF) receptor model (version 5.0) was used to identify and quantify major sources contributing to particulate matter (PM) number concentrations, using PM number size distributions in the range of 13 nm to 10 μm combined with several auxiliary variables, including black carbon (BC), elemental and organic carbon (EC/OC), PM mass concentrations, gaseous pollutants, meteorological, and traffic counts data, collected for about 9 months between August 2014 and 2015 in central Los Angeles, CA. Several parameters, including particle number and volume size distribution profiles, profiles of auxiliary variables, contributions of different factors in different seasons to the total number concentrations, diurnal variations of each of the resolved factors in the cold and warm phases, weekday/weekend analysis for each of the resolved factors, and correlation between auxiliary variables and the relative contribution of each of the resolved factors, were used to identify PM sources. A six-factor solution was identified as the optimum for the aforementioned input data. The resolved factors comprised nucleation, traffic 1, traffic 2 (having a larger mode diameter than traffic 1 factor), urban background aerosol, secondary aerosol, and soil/road dust. Traffic sources (1 and 2) were the major contributor to PM number concentrations, collectively making up to above 60 % (60.8–68.4 %) of the total number concentrations during the study period. Their contribution was also significantly higher in the cold phase compared to the warm phase. Nucleation was another major factor significantly contributing to the total number concentrations (an overall contribution of 17 %, ranging from 11.7 % to 24 %), having a larger contribution during the warm phase than in the cold phase. The other identified factors were urban background aerosol, secondary aerosol, and soil/road dust, with relative contributions of approximately 12 % (7.4–17.1), 2.1 % (1.5–2.5 %), and 1.1 % (0.2–6.3 %), respectively, overall accounting for about 15 % (15.2–19.8 %) of PM number concentrations. As expected, PM number concentrations were dominated by factors with smaller mode diameters, such as traffic and nucleation. On the other hand, PM volume and mass concentrations in the study area were mostly affected by sources having larger mode diameters, including secondary aerosols and soil/road dust. Results from the present study can be used as input parameters in future epidemiological studies to link PM sources to adverse health effects as well as by policy makers to set targeted and more protective emission standards for PM.

scholarly journals Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)

2016 ◽  
Vol 16 (8) ◽  
pp. 4849-4866 ◽  
Author(s):  
Mohammad Hossein Sowlat ◽  
Sina Hasheminassab ◽  
Constantinos Sioutas
Keyword(s):  
Los Angeles ◽  
Matrix Factorization ◽  
Particle Number ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
The Other ◽  
Auxiliary Variables ◽  
Positive Matrix ◽  
Urban Background ◽  
Secondary Aerosol

Abstract. In this study, the positive matrix factorization (PMF) receptor model (version 5.0) was used to identify and quantify major sources contributing to particulate matter (PM) number concentrations, using PM number size distributions in the range of 13 nm to 10 µm combined with several auxiliary variables, including black carbon (BC), elemental and organic carbon (EC/OC), PM mass concentrations, gaseous pollutants, meteorological, and traffic counts data, collected for about 9 months between August 2014 and 2015 in central Los Angeles, CA. Several parameters, including particle number and volume size distribution profiles, profiles of auxiliary variables, contributions of different factors in different seasons to the total number concentrations, diurnal variations of each of the resolved factors in the cold and warm phases, weekday/weekend analysis for each of the resolved factors, and correlation between auxiliary variables and the relative contribution of each of the resolved factors, were used to identify PM sources. A six-factor solution was identified as the optimum for the aforementioned input data. The resolved factors comprised nucleation, traffic 1, traffic 2 (with a larger mode diameter than traffic 1 factor), urban background aerosol, secondary aerosol, and soil/road dust. Traffic sources (1 and 2) were the major contributor to PM number concentrations, collectively making up to above 60 % (60.8–68.4 %) of the total number concentrations during the study period. Their contribution was also significantly higher in the cold phase compared to the warm phase. Nucleation was another major factor significantly contributing to the total number concentrations (an overall contribution of 17 %, ranging from 11.7 to 24 %), with a larger contribution during the warm phase than in the cold phase. The other identified factors were urban background aerosol, secondary aerosol, and soil/road dust, with relative contributions of approximately 12 % (7.4–17.1), 2.1 % (1.5–2.5 %), and 1.1 % (0.2–6.3 %), respectively, overall accounting for about 15 % (15.2–19.8 %) of PM number concentrations. As expected, PM number concentrations were dominated by factors with smaller mode diameters, such as traffic and nucleation. On the other hand, PM volume and mass concentrations in the study area were mostly affected by sources with larger mode diameters, including secondary aerosols and soil/road dust. Results from the present study can be used as input parameters in future epidemiological studies to link PM sources to adverse health effects as well as by policymakers to set targeted and more protective emission standards for PM.

scholarly journals Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 614
Author(s):  
Muhammad Faisal ◽  
Zening Wu ◽  
Huiliang Wang ◽  
Zafar Hussain ◽  
Chenyang Shen
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Matrix Factorization ◽  
Anthropogenic Activities ◽  
Contamination Factor ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
Prevention Measures ◽  
Positive Matrix ◽  
The City

Heavy metals in road dust pose a significant threat to human health. This study investigated the concentrations, patterns, and sources of eight hazardous heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) in the street dust of Zhengzhou city of PR China. Fifty-eight samples of road dust were analyzed based on three methods of risk assessment, i.e., Geo-Accumulation Index (Igeo), Potential Ecological Risk Assessment (RI), and Nemerow Synthetic Pollution Index (PIN). The results exhibited higher concentrations of Hg and Cd 14 and 7 times higher than their background values, respectively. Igeo showed the risks of contamination in a range of unpolluted (Cr, Ni) to strongly polluted (Hg and Cd) categories. RI came up with the contamination ranges from low (Cr, Ni, Cu, Zn, As, and Pb) to extreme (Cd and Hg) risk of contamination. The risk of contamination based on PIN was from safe (Cu, As, and Pb) to seriously high (Cd and Hg). The results yielded by PIN indicated the extreme risk of Cd and Hg in the city. Positive Matrix Factorization was used to identify the sources of contamination. Factor 1 (vehicular exhaust), Factor 2 (coal combustion), Factor 3 (metal industry), and Factor 4 (anthropogenic activities), respectively, contributed 14.63%, 35.34%, 36.14%, and 13.87% of total heavy metal pollution. Metal’s presence in the dust is a direct health risk for humans and warrants immediate and effective pollution control and prevention measures in the city.

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.

Road dust contribution to PM levels – Evaluation of the effectiveness of street washing activities by means of Positive Matrix Factorization

2011 ◽  
Vol 45 (13) ◽  
pp. 2193-2201 ◽  
Author(s):  
Angeliki Karanasiou ◽  
Teresa Moreno ◽  
Fulvio Amato ◽  
Julio Lumbreras ◽  
Adolfo Narros ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
Positive Matrix

scholarly journals Contribution of residential wood combustion and other sources to hourly winter aerosol in Northern Sweden determined by positive matrix factorization

2008 ◽  
Vol 8 (13) ◽  
pp. 3639-3653 ◽  
Author(s):  
P. Krecl ◽  
E. Hedberg Larsson ◽  
J. Ström ◽  
C. Johansson
Keyword(s):  
Matrix Factorization ◽  
Particle Number ◽  
Winter Season ◽  
Positive Matrix Factorization ◽  
Small Towns ◽  
Size Distributions ◽  
Northern Sweden ◽  
Wood Combustion ◽  
Positive Matrix ◽  
Mass Concentrations

Abstract. The combined effect of residential wood combustion (RWC) emissions with stable atmospheric conditions, which frequently occurs in Northern Sweden during wintertime, can deteriorate the air quality even in small towns. To estimate the contribution of RWC to the total atmospheric aerosol loading, positive matrix factorization (PMF) was applied to hourly mean particle number size distributions measured in a residential area in Lycksele during winter 2005/2006. The sources were identified based on the particle number size distribution profiles of the PMF factors, the diurnal contributions patterns estimated by PMF for both weekends and weekdays, and correlation of the modeled particle number concentration per factor with measured aerosol mass concentrations (PM10, PM1, and light-absorbing carbon MLAC) Through these analyses, the factors were identified as local traffic (factor 1), local RWC (factor 2), and local RWC plus long-range transport (LRT) of aerosols (factor 3). In some occasions, the PMF model could not separate the contributions of local RWC from background concentrations since their particle number size distributions partially overlapped. As a consequence, we report the contribution of RWC as a range of values, being the minimum determined by factor 2 and the possible maximum as the contributions of both factors 2 and 3. A multiple linear regression (MLR) of observed PM10, PM1, total particle number, and MLAC concentrations is carried out to determine the source contribution to these aerosol variables. The results reveal RWC is an important source of atmospheric particles in the size range 25–606 nm (44–57%), PM10 (36–82%), PM1 (31–83%), and MLAC (40–76%) mass concentrations in the winter season. The contribution from RWC is especially large on weekends between 18:00 LT and midnight whereas local traffic emissions show similar contributions every day.

scholarly journals Positive matrix factorization of organic aerosol: insights from a chemical transport model

2019 ◽  
Vol 19 (2) ◽  
pp. 973-986 ◽  
Author(s):  
Anthoula D. Drosatou ◽  
Ksakousti Skyllakou ◽  
Georgia N. Theodoritsi ◽  
Spyros N. Pandis
Keyword(s):  
Matrix Factorization ◽  
Transport Model ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Positive Matrix Factorization ◽  
The Other ◽  
Chemical Transport Model ◽  
Positive Matrix ◽  
Aerosol Mass ◽  
High Volatility

Abstract. Factor analysis of aerosol mass spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources accurately. We performed PMF and multilinear engine (ME-2) analysis on the predictions of a state-of-the-art chemical transport model (PMCAMx-SR, Particulate Matter Comprehensive Air Quality Model with extensions – source resolved) during a photochemically active period for specific sites in Europe in an effort to interpret the diverse factors usually identified by PMF analysis of field measurements. Our analysis used the predicted concentrations of 27 OA components, assuming that each of them is “chemically different” from the others. The PMF results based on the chemical transport model predictions are quite consistent (same number of factors and source types) with those of the analysis of AMS measurements. The estimated uncertainty of the contribution of fresh biomass burning is less than 30 % and of the other primary sources less than 40 %, when these sources contribute more than 20 % to the total OA. The PMF uncertainty increases for smaller source contributions, reaching a factor of 2 or even 3 for sources which contribute less than 10 % to the OA. One of the major questions in PMF analysis of AMS measurements concerns the sources of the two or more oxygenated OA (OOA) factors often reported in field studies. Our analysis suggests that these factors include secondary OA compounds from a variety of anthropogenic and biogenic sources and do not correspond to specific sources. Their characterization in the literature as low- and high-volatility factors is probably misleading, because they have overlapping volatility distributions. However, the average volatility of the one often characterized as a low-volatility factor is indeed lower than that of the other (high-volatility factor). Based on the analysis of the PMCAMx-SR predictions, the first oxygenated OA factor includes mainly highly aged OA transported from outside Europe, but also highly aged secondary OA from precursors emitted in Europe. The second oxygenated OA factor contains fresher secondary organic aerosol from volatile, semivolatile, and intermediate volatility anthropogenic and biogenic organic compounds. The exact contribution of these OA components to each OA factor depends on the site and the prevailing meteorology during the analysis period.

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