Chemical mass balance source apportionment of fine and PM<sub>10</sub> in the Desert Southwest, USA

Andrea L. Clements; Matthew P. Fraser; Pierre Herckes; Paul A. Solomon; Andrea L. Clements; Matthew P. Fraser; Pierre Herckes; Paul A. Solomon

doi:10.3934/environsci.2016.1.115

AIMS Environmental Science

2016, Volume 3, Issue 1: 115-132. doi: 10.3934/environsci.2016.1.115

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Research article Special Issues

Chemical mass balance source apportionment of fine and PM₁₀ in the Desert Southwest, USA

1.
Department of Civil and Environmental Engineering, Rice University, Houston, TX 77005, USA
2.
Present Address: Department of Atmospheric Sciences, Colorado State University, Ft Collins, CO 80523, USA
3.
School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA
4.
School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
5.
National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Las Vegas, NV 89119, USA

Received: 15 December 2015 Accepted: 23 February 2016 Published: 07 March 2016

The Desert Southwest Coarse Particulate Matter Study was undertaken in Pinal County, Arizona, to better understand the origin and impact of sources of fine and coarse particulate matter (PM) in rural, arid regions of the U.S. southwestern desert. The desert southwest experiences some of the highest PM₁₀ mass concentrations in the country. To augment previously reported results, 6-week aggregated organic speciation data that included ambient concentrations of n-alkanes, polycyclic aromatic hydrocarbons, organic acids, and saccharides were used in chemical mass balance modeling (CMB). A set of re-suspended soil samples were analyzed for specific marker species to provide locally-appropriate source profiles for the CMB analysis. These profiles, as well as previously collected plant and fungal spore profiles from the region, were combined with published source profiles for other relevant sources and used in the CMB analysis. The six new region-specific source profiles included both organic and inorganic species for four crustal material sources, one plant detritus source, and one fungal spore source.
Results indicate that up to half of the ambient PM_2.5 was apportioned to motor vehicles with the highest regional contribution observed in the small urban center of Casa Grande. Daily levels of apportioned crustal material accounted for up to 50% of PM_2.5 mass with the highest contributions observed at the sites closest to active agricultural areas. Apportioned secondary PM, biomass burning, and road dust typically contributed less than 35% as a group to the apportioned PM_2.5 mass. Crustal material was the primary source apportioned to PM₁₀ and accounted for between 50–90% of the apportioned mass. Of the other sources apportioned to PM₁₀, motor vehicles and road dust were the largest contributors at the urban and one of the rural sites, whereas road dust and meat cooking operations were the largest contributors at the other rural site.

Keywords:

organic speciation,
crustal material source profiles

Citation: Andrea L. Clements, Matthew P. Fraser, Pierre Herckes, Paul A. Solomon. Chemical mass balance source apportionment of fine and PM10 in the Desert Southwest, USA[J]. AIMS Environmental Science, 2016, 3(1): 115-132. doi: 10.3934/environsci.2016.1.115

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Abstract

References

[1]	Arizona Department Environmental Quality. Analysis of PM2.5 Exceedences in Pinal County Arizona: Demonstration that PM2.5 Concentration are Driven by Local Sources of PM10 Near the Cowtown Monitor, 2010. Available from: http://www.pinalcountyaz.gov/AirQuality/ Documents/Other%20EPA%20Regulatory%20Actions/CowtownTechnicalWhitePaper.pdf
[2]	Federal Register, 71 FR 61143 National Ambient Air Quality Standards for PM, 2006. Available from: https://www.gpo.gov/fdsys/granule/FR-2006-10-17/06-8477
[3]	Pinal County Air Quality Control District. Pinal County Air Quality Control District Source Apportionment Study, 2003. Available from: http://pinalcountyaz.gov/Departments/AirQuality/ Documents/Monitoring%20Network/pinal_speciation_study.pdf
[4]	Arizona Department Environmental Quality. Arizona Air Quality Designations Technical Support Document: Boundary Recommendations for the Pinal County 24-hour PM10 Nonattainment Area. Phoenix, AZ: Arizona Department of Environmental Quality, 2010. Available from: http://pinalcountyaz.gov/departments/airquality/documents/other%20epa%20regulatory%20actions/ pinalcountypm10nonattainmtrecommtsd.pdf
[5]	Clements AL, Fraser MP, Upadhyay N, et al. (2013) Summertime characterization of fine and coarse particulate matter in the desert southwest - Arizona, USA. J Air Waste Manag Assoc 63: 764-772.
[6]	U.S. Environmental Protection Agency. AirData: Access to Air Pollution Data, 2010. Available from: http://www.epa.gov/airdata/
[7]	Clements AL, Fraser MP, Upadhyay N, et al. (2014) Chemical characterization of coarse particulate matter in the desert Southwest - Pinal County Arizona, USA. Atmos Pollut Res 5: 52-61.
[8]	Upadhyay N, Clements AL, Fraser MP, et al. (2015) Size-differentiated chemical composition of resuspended soil dust from the desert southwest United States. Aerosol Air Qual Res 15: 387-398.
[9]	Derrick M, Moyers J (1981) Precise and sensitive water-soluble ion extraction method for aerosol samples collected on polytetrafluoroethylene filters. Anal Lett Part A Chem Anal 14: 1637-1652.
[10]	Birch ME, Cary RA (1996) Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol Sci Technol 25: 221-241. doi: 10.1080/02786829608965393
[11]	Medeiros PM, Simoneit BRT (2007) Analysis of sugars in environmental samples by gas chromatography-mass spectrometry. J Chromatogr A 1141: 271-278. doi: 10.1016/j.chroma.2006.12.017
[12]	Chow JC, Watson JG, Kuhns H, et al. (2004) Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study. Chemosphere 54: 185-208. doi: 10.1016/j.chemosphere.2003.07.004
[13]	Schauer JJ, Kleeman MJ, Cass GR, et al. (1999) Measurement of emissions from air pollution sources. 1. C1 through C29 organic compounds from meat charbroiling. Environ Sci Technol 33: 1566-1577.
[14]	Schauer JJ, Kleeman MJ, Cass GR, et al. (1999) Measurement of emissions from air pollution sources. 2. C-1 through C-30 organic compounds from medium duty diesel trucks. Environ Sci Technol 33: 1578-1587.
[15]	Schauer JJ, Kleeman MJ, Cass GR, et al. (2002) Measurement of emissions from air pollution sources. 5. C1-C32 organic compounds from gasoline-powered motor vehicles. Environ Sci Technol 36: 1169-1180.
[16]	Jia Y, Fraser MP (2011) Characterization of saccharides in size-fractionated ambient particulate matter and aerosol sources: The contribution of Primary Biological Aerosol Particles (PBAPs) and soil to ambient particulate matter. Environ Sci Technol 45: 930-936. doi: 10.1021/es103104e
[17]	US Environmental Protection Agency. EPA-CMB8.2 Users Manual, 2004. Available from: https://www3.epa.gov/scram001/models/receptor/EPA-CMB82Manual.pdf
[18]	Eldred B. Evaluation of the equation for soil composite, 2003. Available from: http://vista.cira.colostate.edu/IMPROVE/Publications/GrayLit/023_SoilEquation/Soil_Eq_Evaluation.pdf
[19]	Taylor SR, McLennan SM. (1995) The geochemical evolution of the continental crust. Rev Geophys 33: 241-265. doi: 10.1029/95RG00262
[20]	Watson JG, Chow JC (2001) Source characterization of major emission sources in the Imperial and Mexicali Valleys along the US/Mexico border. Sci Total Environ 276: 33-47.
[21]	Turpin BJ, Lim HJ (2001) Species contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass. Aerosol Sci Technol 35: 602-610.
[22]	Jia YL, Clements AL, Fraser MP (2010) Saccharide composition in atmospheric particulate matter in the southwest US and estimates of source contributions. J Aerosol Sci 41: 62-73. doi: 10.1016/j.jaerosci.2009.08.005
[23]	Zhang KM, Wexler AS (2004) Evolution of Particle Number Distribution near Roadways. Part I: Analysis of Aerosol Dynamics and Its Implications for Engine Emission Measurement. Atmos Environ 38: 6643-6653.
[24]	Solomon.PA, Hopke PK, Froines J, et al. (2008) Key Scientific and Policy- and Health-Relevant Findings from EPA’s Particulate Matter Supersites Program and Related Studies: An Integration and Synthesis of Results. J Air Waste Manag Assoc 58: S1-S92. doi: 10.1080/10473289.2008.10465571

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