Using Aerosol Light Absorption Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter

A source apportionment study was performed for particulate matter in the small village of Roveredo, Switzerland, where more than 70% of the households use wood burning for heating purposes. A two-lane trans-Alpine highway passes through the village and contributes to the total aerosol burden in the...

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Veröffentlicht in:Environmental science & technology 2008-05, Vol.42 (9), p.3316-3323
Hauptverfasser: Sandradewi, Jisca, Prévôt, Andre S. H, Szidat, Sönke, Perron, Nolwenn, Alfarra, M. Rami, Lanz, Valentin A, Weingartner, Ernest, Baltensperger, Urs
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container_end_page 3323
container_issue 9
container_start_page 3316
container_title Environmental science & technology
container_volume 42
creator Sandradewi, Jisca
Prévôt, Andre S. H
Szidat, Sönke
Perron, Nolwenn
Alfarra, M. Rami
Lanz, Valentin A
Weingartner, Ernest
Baltensperger, Urs
description A source apportionment study was performed for particulate matter in the small village of Roveredo, Switzerland, where more than 70% of the households use wood burning for heating purposes. A two-lane trans-Alpine highway passes through the village and contributes to the total aerosol burden in the area. The village is located in a steep Alpine valley characterized by strong and persistent temperature inversions during winter, especially from December to February. During two winter and one early spring campaigns, a seven-wavelength aethalometer, high volume (HIVOL) samplers, an Aerodyne quadrupole aerosol mass spectrometer (AMS), an optical particle counter (OPC), and a Sunset Laboratory OCEC analyzer were deployed to study the contribution of wood burning and traffic aerosols to particulate matter. A linear regression model of the carbonaceous particulate mass in the submicrometer size range CM(PM1) as a function of aerosol light absorption properties measured by the aethalometer is introduced to estimate the particulate mass from wood burning and traffic (PMwb, PMtraffic). This model was calibrated with analyses from the 14C method using HIVOL filter measurements. These results indicate that light absorption exponents of 1.1 for traffic and 1.8–1.9 for wood burning calculated from the light absorption at 470 and 950 nanometers should be used to obtain agreement of the two methods regarding the relative wood burning and traffic emission contributions to CM(PM1) and also to black carbon. The resulting PMwb and PMtraffic values explain 86% of the variance of the CM(PM1) and contribute, on average, 88 and 12% to CM(PM1), respectively. The black carbon is estimated to be 51% due to wood burning and 49% due to traffic emissions. The average organic carbon/total carbon (OC/TC) values were estimated to be 0.52 for traffic and 0.88 for wood burning particulate emissions.
doi_str_mv 10.1021/es702253m
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During two winter and one early spring campaigns, a seven-wavelength aethalometer, high volume (HIVOL) samplers, an Aerodyne quadrupole aerosol mass spectrometer (AMS), an optical particle counter (OPC), and a Sunset Laboratory OCEC analyzer were deployed to study the contribution of wood burning and traffic aerosols to particulate matter. A linear regression model of the carbonaceous particulate mass in the submicrometer size range CM(PM1) as a function of aerosol light absorption properties measured by the aethalometer is introduced to estimate the particulate mass from wood burning and traffic (PMwb, PMtraffic). This model was calibrated with analyses from the 14C method using HIVOL filter measurements. 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A two-lane trans-Alpine highway passes through the village and contributes to the total aerosol burden in the area. The village is located in a steep Alpine valley characterized by strong and persistent temperature inversions during winter, especially from December to February. During two winter and one early spring campaigns, a seven-wavelength aethalometer, high volume (HIVOL) samplers, an Aerodyne quadrupole aerosol mass spectrometer (AMS), an optical particle counter (OPC), and a Sunset Laboratory OCEC analyzer were deployed to study the contribution of wood burning and traffic aerosols to particulate matter. A linear regression model of the carbonaceous particulate mass in the submicrometer size range CM(PM1) as a function of aerosol light absorption properties measured by the aethalometer is introduced to estimate the particulate mass from wood burning and traffic (PMwb, PMtraffic). This model was calibrated with analyses from the 14C method using HIVOL filter measurements. These results indicate that light absorption exponents of 1.1 for traffic and 1.8–1.9 for wood burning calculated from the light absorption at 470 and 950 nanometers should be used to obtain agreement of the two methods regarding the relative wood burning and traffic emission contributions to CM(PM1) and also to black carbon. The resulting PMwb and PMtraffic values explain 86% of the variance of the CM(PM1) and contribute, on average, 88 and 12% to CM(PM1), respectively. The black carbon is estimated to be 51% due to wood burning and 49% due to traffic emissions. The average organic carbon/total carbon (OC/TC) values were estimated to be 0.52 for traffic and 0.88 for wood burning particulate emissions.</abstract><cop>Washington, DC</cop><pub>American Chemical Society</pub><pmid>18522112</pmid><doi>10.1021/es702253m</doi><tpages>8</tpages></addata></record>
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subjects Aerosols
Aerosols - analysis
Air Pollutants - analysis
Air pollution
Airborne particulates
Applied sciences
Carbon
Carbon - analysis
Carbon - chemistry
Dust
Emissions
Environmental Measurements Methods
Environmental Monitoring - methods
Environmental science
Exact sciences and technology
Fireplaces
Human immunodeficiency virus
Light
Particle Size
Particulate Matter - analysis
Pollution
Regression Analysis
Seasons
Sorption
Switzerland
Temperature
Time Factors
Wood
title Using Aerosol Light Absorption Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter
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