Use of advanced receptor modelling for analysis of an intensive 5-week aerosol sampling campaign

Buset, Kathleen C.; Evans, Greg J.; Leaitch, W. R.; Brook, Jeffrey R.; Toom-Sauntry, D.

doi:10.1016/j.atmosenv.2005.12.074

Cited by 35 publications

(30 citation statements)

References 32 publications

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“…The collection efficiency of particles by the oven is assumed to be 100% for this dataset. This assumption is based on the comparison of the sulphate mass concentrations from the Q-AMS with those from the ParticleIn-Liquid-Sampler (PILS; described below), and it is consistent with previous results with this Q-AMS (Rupakheti et al, 2005;Buset et al, 2006;Phinney et al, 2006;Langley et al, 2010); more acidic sulphate, as here, is efficiently sampled. Analysis of AMS data was performed using the Deluxe 1.29 IGOR data analysis package (Allan et al, 2003) with a batch file (used for quantitative calibration) and fragmentation file (used for identification of chemical species present on the aerosol) customized to this data set.…”

Section: Instrumentationsupporting

confidence: 71%

“…Aerosol particles were collected in water using a PILS and analyzed for their major water soluble inorganic chemical components onboard with two ion chromatographs (IC). The PILS collection system is described briefly here, and it is similar to that described by Orsini et al (2003); this particular PILS has been previously documented (Buset et al, 2006). Briefly, the system used two Dionex Ion Chromatographs (ICS 2000) with eluent generation, temperature control and degassing for anions and cations.…”

Section: Instrumentationmentioning

confidence: 99%

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Cloud albedo increase from carbonaceous aerosol

et al. 2010

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Abstract. Airborne measurements from two consecutive days, analysed with the aid of an aerosol-adiabatic cloud parcel model, are used to study the effect of carbonaceous aerosol particles on the reflectivity of sunlight by water clouds. The measurements, including aerosol chemistry, aerosol microphysics, cloud microphysics, cloud gust velocities and cloud light extinction, were made below, in and above stratocumulus over the northwest Atlantic Ocean. On the first day, the history of the below-cloud fine particle aerosol was marine and the fine particle sulphate and organic carbon mass concentrations measured at cloud base were 2.4 µg m −3 and 0.9 µg m −3 respectively. On the second day, the below-cloud aerosol was continentally influenced and the fine particle sulphate and organic carbon mass concentrations were 2.3 µg m −3 and 2.6 µg m −3 respectively. Over the range 0.06-0.8 µm diameter, the shapes of the below-cloud size distributions were similar on both days and the number concentrations were approximately a factor of two higher on the second day. The cloud droplet number concentrations (CDNC) on the second day were approximately three times higher than the CDNC measured on the first day. Using the parcel model to separate the influence of the differences in gust velocities, we estimate from the vertically integrated cloud light scattering measurements a 6% increase in the cloud albedo principally due to the increase in the carbonaceous components on the second day. Assuming no additional absorption by this aerosol, a 6% albedo increase translates to a local daytime radiative cooling of ∼ 12 W m −2 . This reCorrespondence to: W. R. Leaitch (richard.leaitch@ec.gc.ca) sult provides observational evidence that the role of anthropogenic carbonaceous components in the cloud albedo effect can be much larger than that of anthropogenic sulphate, as some global simulations have indicated.

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Section: Instrumentationsupporting

confidence: 71%

Section: Instrumentationmentioning

confidence: 99%

Cloud albedo increase from carbonaceous aerosol

et al. 2010

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“…In order to identify an algorithm to solve the more general sums-of-products problem, a tool with a more flexible approach for the fitting of multilinear models (ME) was developed by Paatero (1999). ME-2 has been used in many prior source apportionment studies in the Arctic, Phoenix, Seattle, a Pittsburgh supersite, a Baltimore supersite and Toronto (Buset et al, 2006;Ogulei et al, 2005;Kim et al, 2004a;Zhou et al, 2004;Ramadan et al, 2003;Xie et al, 1999) and can be used to solve multilinear and quasi-multilinear problems. Both PMF2 and ME-2 include non-negativity constraints on the factors in order to decrease rotational freedom.…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, a more flexible multivariate analysis tool, the multilinear engine (ME), was developed to solve a variety of multilinear problems (Paatero, 1999). ME has already been applied in several studies because of its flexibility (Buset et al, 2006;Ogulei et al, 2005;Zhou et al, 2004;YliTuomi et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Source Apportionment of PM2.5 Using PMF2 and EPA PMF Version 2

Hwang¹,

Hopke²

2011

Asian J. Atmos. Environ

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The positive matrix factorization (PMF2) and multilinear engine (ME2) models have been shown to be powerful environmental analysis techniques and have been successfully applied to the assessment of ambient particulate matter (PM) source contributions. Because these models are difficult to apply practically, the US EPA developed a more user-friendly version of the PMF. The initial version of the EPA PMF model does not provide any rotational capabilities; for this reason, the model was upgraded to include rotational functions in the EPA PMF ver. 2.0. In this study, PMF and EPA PMF modeling identified ten particulate matter sources including secondary sulfate I, vehicle gasoline, secondary sulfate II, secondary nitrate, secondary sulfate III, incinerators, aged sea salt, airborne soil particles, oil combustion, and diesel emissions. All of the source profiles determined by the two models showed excellent agreement. The calculated average concentrations of PM 2.5 were consistent between the PMF2 and EPA PMF (17.94±0.30 μg/m 3 and 17.94±0.30 μg/m 3 , respectively). Also, each set of estimated source contributions of the PMF2 and EPA PMF showed good agreement. The results from the new EPA PMF version applying rotational functions were consistent with those of PMF2. Therefore, the updated version of EPA PMF with rotational capabilities will provide more reasonable solutions compared with those of PMF2 and can be more widely applied to air quality management.

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“…To reduce rotational ambiguity, multi-linear models that include other parameters (e.g., speed, wind direction, and temporal factors) have been introduced in ME-2 (Paatero and Hopke, 2002;Kim et al, 2003;Buset et al, 2006). introduced hourly Radon-222 (radon) concentrations observed at Richmond, NSW, to a multi-linear model as a combined proxy for (i) the degree of terrestrial influence on an air mass (an air mass fetch effect), and (ii) the degree of dilution in the atmosphere due to the diurnal evolution of the atmospheric boundary layer (ABL).…”

Section: Introductionmentioning

confidence: 99%

Using Radon-222 as an Indicator of Atmospheric Mixing Depth in ME-2 for PM2.5 Source Apportionment

Crawford

Chambers

Cohen

et al. 2015

Aerosol Air Qual. Res.

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We isolated diurnal timescale contributions to a 6-year hourly radon record and incorporated them in ME-2 as a proxy for changes in atmospheric mixing depth in an attempt to improve the source apportionment of fine atmospheric particulate matter (PM 2.5 ). Results from this radon-based implementation of ME-2 are directly compared with the more traditional ME-2 implementation where wind speed is used, as a proxy for changes in mixing depth. The radon-based version more accurately reproduced daily PM 2.5 source contributions, as evidenced by better correlations with the results from the corresponding bi-linear model. The versions of ME-2 employed in this study were modified to account for calm wind conditions separately, and a recently updated solution approach was adopted.Source apportionment for the radon-based ME-2 implementation was most successful for the finer, primary emissions (Smoke, Autos, Industry) that are more easily suspended and whose concentrations are more directly tied to changes in atmospheric mixing depth. Incorporation of the diurnal radon signal in ME-2 improved the estimated source strength distributions of the Smoke, Autos and Industry sources with respect to the township of Muswellbrook. It also resulted in a more consistent anti-correlation between these 3 source types and atmospheric mixing depth than for the wind speed case. These results confirm that near surface radon concentration is more closely tied to atmospheric mixing depth (and therefore pollutant concentrations) than wind speed.The measurement site for this study is a small township in a rural setting, with nearby power stations and open-cut coal mines. Consequently, the distribution and characteristics of anthropogenic aerosol sources are very different than for a typical urban or industrial setting. This is reflected in lower correlation between the multi-linear models and the corresponding bi-linear models, indicating that the performance of multi-linear models is affected by the nature of the distribution of sources.

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Use of advanced receptor modelling for analysis of an intensive 5-week aerosol sampling campaign

Cited by 35 publications

References 32 publications

Cloud albedo increase from carbonaceous aerosol

Cloud albedo increase from carbonaceous aerosol

Comparison of Source Apportionment of PM2.5 Using PMF2 and EPA PMF Version 2

Using Radon-222 as an Indicator of Atmospheric Mixing Depth in ME-2 for PM2.5 Source Apportionment

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