Cooper Environmental Services

Source Apportionment of Urban Particulate Matter using Hourly Resolved Trace Metals. Oi`ganics, and Inorganic Aerosol Components


Courtesy of Cooper Environmental Services

5    'Southern Ontario Centre for Atmospheric Aerosol Research. University of Toronto, Toronto, M5S 3E5. Canada Correspondence to: Cheol-Heon Jeong (chjeongiiiiutorontoca) and Greg J. Evans (
Abstract. Source apportionment analysis of hourly resolved particulate matter (PM) speciauon data was performed using positive matrix factorization (PMF). The data were measured at an urban site m downtown Toronto. Canada during two
10    campaign periods (April-July. 2013; November. 2013-February. 2014), and mcluded trace metals, black carbon, and mass
spectra for organic and inorganic species (PMFfou). The chemical composition was measured by collocated high time resolution instrumentation, including an Aerosol Chemical Speciauon Monitor, an Xact metals monitor, and a seven-wavelength Aethalometer. Separate PMF analyses were conducted using the trace metal only data (PMFmu) and organic mass spectra only (PMFarg). and compared with the PMFm results. Comparison of these three PMF analyses demonstrated that the full analysis
15    offered many advantages in the apportionment of local and regional sources compared to using the orgamc or metals data
individually. In combining the high time resolution data, this analysis enabled l) the quantification of metal-rich sources of PM;.; (PM 2.5 um). ii) the resolution of more robust factor profiles and contributions, and in) the identification of additional orgamc aerosol sources.
Nine factors were identified through the PMFVta analysis: five local factors (i.e. Road Dust Primary Vehicle Emissions. Tire
20    Wear. Cooking, and Industrial Sector) and four regional factors (i.e. Biomass Burning. Oxidised Organics, Sulphate and
Oxidised Organics. and Nitrate and Oxidised Organics). The majority of the metal emissions (83%) and almost half of the black carbon (49%) were associated with the three traffic-related factors which, on average, contributed a minority (17%) of the overall PMu mass. Strong seasonal patterns were observed for the traffic-related emissions: higher contributions of resuspended road dust in spring vs. a winter high for ore wear related emissions. Biomass Burning contributed the majority of the PM;.5 mass
25    (52%) m June and July due to a major forest fire event. Much of this mass was due to photochemical aging of the biomass burning aerosol. On average, industrially related factors contributed almost half (49%) of the PM: 5; most of this mass was secondary aerosol species. Nitrate coupled with highly oxidised organics was the largest contributor, accounting for 30% of PM21 on average, with higher levels in winter and at night. Including the temporal variabilities of inorganic ion; and trace metals m the PMFm analysis provided additional structure to subdivide the low volatility oxidised orgamc aerosol mto three sources.
30    Resuspended road dust was identified as a potential source of aged organic aerosol.
The novelty of this study is the application of PMF receptor modeling to hourly resolved trace metals in conjunction with orgamc mass spectra, inorganic species, and black carbon for different seasons, and the comparison of separate PMF analyses apphed to metals or organics alone. The inclusion of these different types of hourly data allowed more robust apportionment of PM sources, as compared to analysing organic or metals data individually.

  1. Introduction

Particulate matter (PM) has been shown to have negative impacts on human health, atmospheric nubility, and radiative forcmg {e.g. EPCC, 2013; HEL 2013). The source identification and qxiantification of particulate matter (PM) is crucial to understanding 5    aerosol chemical processes and developing effective PM abatement strategies. Receptor modeling provides a method to
distinguish the relative contributions of PM sources based upon measurements at receptor sites. Positive Matrix Factorization (PMF), a bilinear multivariate receptor modeL is widely used to identify sources of PM m the atmosphere and provide the contribution of each source. Typically, receptor modeling of 24-hr integrated PM chemical speciafion data has been used for understanding the relative contributions of different sources and providing an overview of long-term temporal or spatial patterns
10    of major source categories (e.g. Xie et al.. 1999; Lee et al.. 2003; Chen et aL 2011; Jeong et iL, 2013).
Receptor modeling of high time resolution data (hourly or sub-hourly) can be used to better resolve and understand processes and sources with more rapid temporality, and thereby obtain more accurate source apportionment results. Organic aerosol (OA). a major component of PM. is not fully understood due to its complex composition and properties. Over the past years, source apportionment using high time resolution OA mass spectra measured by an aerosol mass spectrometer (AMS) or an
15    aerosol chemical speciation monitor (ACSM) has been a useful approach to reduce the dimensionality of complex organic
fractions mto more easily interpretable OA factors (e.g. Ulbnch et al.. 2009; He et al.. 2010; Ng et al., 2011; Crippa et al., 2013; Bougiauou et al.. 2014; Lee et al.. 2015). Three primary factors: hydrocarbon-like organic aerosol (HOA). cookmg organic aerosol (COA), and biomass burning organic aerosol (BBOA). are commonly identified in many locations. Additionally, two secondary- organic factors: low-volatility oxygenated organic aerosol (LV-OOA) and semi-volatile oxygenated organic aerosol
20    (SV-OOA). have also been observed (e.g. Zhang et al., 2007; Jimenez et al., 2009; Ng et al., 2010). More recently. PMF was applied to combined organic and inorganic mass spectra to find additional organic source types and provide insight mto the characteristics and processes of these sources (Chang et al., 2011; Sunetal., 2012; McGuireetal.. 2014). McCnure et al. (2014) reported that oxygenated OA factors related to sulphate and nitrate were more effectively resolved by a full mass spectra method including inorganics. Thus, additional information (i.e. trace metals) may be useful m resolving a more complete understanding
25    of the sources of OA.
Typically, trace metals comprise a mmor component of ambient PM:.;(PM smaller than 2.5 urn in aerodynamic diameter) on a mass basis. Nevertheless, these mmor elements are very useful as they can act as identifying markers of PM2.5 sources. Highly time resolved measurements of trace metal components add greater temporal variabilities to certain source apportionment analysis, which can assist m the identification of local sources such as traffic and industry. Using one- or two-hour resolution
30    metal data m receptor modeling analyses has proven to be useful m the identification of local aerosol sources m urban areas
(Richard etal.. 2011;DalTOstoet al., 2013). These previous studies found local urban dust factors such as road dust, brake dust, and local industrial sources, which typically occur sporadically and last only a few hours at most. However, the low inherent concentrations of key marker metals can be a challenge when applying PMF to metals data.
This study examines three different PMF approaches using hourly time resolution data and compares the identification and
35    mass attribution of the PM sources thereby achieved. The data considered mcluded trace metals, organic''inorganics species, and black carbon measured in an urban area during warm (April-July) and cold (December-February) months. Separate PMF analyses were executed for the l) trace metals, ii) organic mass spectra, and lii) combmed metals, organics inorganics, and black carbon (BO A number of advantages to using this combined analysis were thereby identified m terms of l) quantification of metal-nch sources of PM25 such as road dust, ii) resolution of more robust factor profiles for difficult to separate sources such as
40    HOA vs. COA.or OOA vs. BBOA. and lii) subdivision of sources of aged organic aerosol. The results from this study-provide additional insight into sources of fine particle pollutants that have high temporal variations and thereby support the development of more effective control strategies for ambient pollutants.

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