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

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.

Download Full-text

Contribution of residential wood combustion to hourly winter aerosol in Northern Sweden determined by positive matrix factorization

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-5725-2008 ◽

2008 ◽

Vol 8 (2) ◽

pp. 5725-5760 ◽

Cited By ~ 2

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 is a frequent occurrence 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, the positive matrix factorization (PMF) method 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, it was difficult to detach the contributions of local RWC from background concentrations since their particle number size distributions partially overlapped and the model was not able to separate these two sources. 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.

Download Full-text

Comments to "Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)"

10.5194/acp-2015-959-rc2 ◽

2016 ◽

Author(s):

Anonymous

Keyword(s):

Los Angeles ◽

Source Apportionment ◽

Matrix Factorization ◽

Particle Number ◽

Positive Matrix Factorization ◽

Positive Matrix ◽

Ambient Particle

Download Full-text

Analysis of indoor particle size distributions in an occupied townhouse using positive matrix factorization

Indoor Air ◽

10.1111/j.1600-0668.2006.00418.x ◽

2006 ◽

Vol 16 (3) ◽

pp. 204-215 ◽

Cited By ~ 58

Author(s):

D. Ogulei ◽

P. K. Hopke ◽

L. A. Wallace

Keyword(s):

Particle Size ◽

Matrix Factorization ◽

Positive Matrix Factorization ◽

Size Distributions ◽

Particle Size Distributions ◽

Positive Matrix

Download Full-text

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

10.5194/acp-2015-959 ◽

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.

Download Full-text

Analysis of Ambient Particle Size Distributions Using Unmix and Positive Matrix Factorization

Environmental Science & Technology ◽

10.1021/es030310s ◽

2004 ◽

Vol 38 (1) ◽

pp. 202-209 ◽

Cited By ~ 83

Author(s):

Eugene Kim ◽

Philip K. Hopke ◽

Timothy V. Larson ◽

David S. Covert

Keyword(s):

Particle Size ◽

Matrix Factorization ◽

Positive Matrix Factorization ◽

Size Distributions ◽

Particle Size Distributions ◽

Positive Matrix ◽

Ambient Particle

Download Full-text

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

10.5194/acp-2015-959-rc1 ◽

2016 ◽

Author(s):

Anonymous

Keyword(s):

Los Angeles ◽

Source Apportionment ◽

Matrix Factorization ◽

Particle Number ◽

Positive Matrix Factorization ◽

Positive Matrix ◽

Ambient Particle

Download Full-text

Supplementary material to "Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)"

10.5194/acp-2015-959-supplement ◽

2016 ◽

Author(s):

M. H. Sowlat ◽

S. Hasheminassab ◽

C. Sioutas

Keyword(s):

Los Angeles ◽

Source Apportionment ◽

Matrix Factorization ◽

Particle Number ◽

Positive Matrix Factorization ◽

Positive Matrix ◽

Supplementary Material ◽

Ambient Particle

Download Full-text

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

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-4849-2016 ◽

2016 ◽

Vol 16 (8) ◽

pp. 4849-4866 ◽

Cited By ~ 66

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.

Download Full-text

Application of positive matrix factorization to on-road measurements for source apportionment of diesel- and gasoline-powered vehicle emissions in Mexico City

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-3629-2010 ◽

2010 ◽

Vol 10 (8) ◽

pp. 3629-3644 ◽

Cited By ~ 28

Author(s):

D. A. Thornhill ◽

A. E. Williams ◽

T. B. Onasch ◽

E. Wood ◽

S. C. Herndon ◽

...

Keyword(s):

Mexico City ◽

Matrix Factorization ◽

Vehicle Emissions ◽

Particle Number ◽

Positive Matrix Factorization ◽

Emission Factors ◽

Receptor Modeling ◽

Mobile Laboratory ◽

Positive Matrix ◽

Engine Exhaust

Abstract. The goal of this research is to quantify diesel- and gasoline-powered motor vehicle emissions within the Mexico City Metropolitan Area (MCMA) using on-road measurements captured by a mobile laboratory combined with positive matrix factorization (PMF) receptor modeling. During the MCMA-2006 ground-based component of the MILAGRO field campaign, the Aerodyne Mobile Laboratory (AML) measured many gaseous and particulate pollutants, including carbon dioxide, carbon monoxide (CO), nitrogen oxides (NOx), benzene, toluene, alkylated aromatics, formaldehyde, acetaldehyde, acetone, ammonia, particle number, fine particulate mass (PM2.5), and black carbon (BC). These serve as inputs to the receptor model, which is able to resolve three factors corresponding to gasoline engine exhaust, diesel engine exhaust, and the urban background. Using the source profiles, we calculate fuel-based emission factors for each type of exhaust. The MCMA's gasoline-powered vehicles are considerably dirtier, on average, than those in the US with respect to CO and aldehydes. Its diesel-powered vehicles have similar emission factors of NOx and higher emission factors of aldehydes, particle number, and BC. In the fleet sampled during AML driving, gasoline-powered vehicles are found to be responsible for 97% of total vehicular emissions of CO, 22% of NOx, 95–97% of each aromatic species, 72–85% of each carbonyl species, 74% of ammonia, negligible amounts of particle number, 26% of PM2.5, and 2% of BC; diesel-powered vehicles account for the balance. Because the mobile lab spent 17% of its time waiting at stoplights, the results may overemphasize idling conditions, possibly resulting in an underestimate of NOx and overestimate of CO emissions. On the other hand, estimates of the inventory that do not correctly account for emissions during idling are likely to produce bias in the opposite direction.The resulting fuel-based estimates of emissions are lower than in the official inventory for CO and NOx and higher for VOCs. For NOx, the fuel-based estimates are lower for gasoline-powered vehicles but higher for diesel-powered ones compared to the official inventory. While conclusions regarding the inventory should be interpreted with care because of the small sample size, 3.5 h of driving, the discrepancies with the official inventory agree with those reported in other studies.

Download Full-text