2007 Progress Report: Using Carbohydrates as Molecular Markers to Determine the Contribution of Agricultural Soil to Ambient Fine and Course PM

EPA Grant Number: R832164
Title: Using Carbohydrates as Molecular Markers to Determine the Contribution of Agricultural Soil to Ambient Fine and Course PM
Investigators: Fraser, Matthew P.
Institution: Rice University
Current Institution: Arizona State University - Main Campus
EPA Project Officer: Chung, Serena
Project Period: December 1, 2004 through November 30, 2007 (Extended to November 30, 2010)
Project Period Covered by this Report: December 1, 2006 through November 30, 2007
Project Amount: $441,299
RFA: Source Apportionment of Particulate Matter (2004) RFA Text |  Recipients Lists
Research Category: Particulate Matter , Air Quality and Air Toxics , Air

Objective:

The overall goal of the proposed project is to fully develop, employ and verify a technique to quantify the contribution of agricultural soils entrained in the atmosphere to ambient fine and coarse particulate matter (PM). This project will test the hypothesis that the carbohydrate species present in agricultural soils are chemically distinct from organic components in native soils as a result of soil improvements designed to raise the organic content and productivity of agricultural soil. This project will focus on comparison of the concentrations of marker species in agricultural soils to unimproved soils, and attempt to separate agricultural emissions from other fugitive dust sources (such as windblown dusts, unpaved road dusts or construction dusts).

Progress Summary:

Year 3 research focused on source attribution calculations of fine particulate matter samples collected at one urban site (Dallas) and one rural site (San Augustine) in East Texas.  The move by the Principal Investigator from Rice University to Arizona State University delayed a planned second field measurement campaign which was originally scheduled for the third year of the project but is now being conducted in the fourth year. 

Source Apportionment using Multivariate Receptor Modeling Techniques

Positive Matrix Factorization (PMF) is a statistical analysis source apportionment model, which has been widely used in the past with elemental data and to a more limited extent, on organic molecular markers.  Since sugars have been proposed as one of the primary markers, the contribution of agricultural sources and forest fires through soil resuspension and biomass burning, the contribution of entrainment of agricultural soils and biomass burning to ambient levels of fine particulate matter can be isolated by performing PMF calculations using the ambient compound dataset generated in the project field experiments.  By adding the unique molecular markers from agricultural soils, the understanding of the various sources of crustal material can be further refined.

PMF is a multivariate receptor modeling technique that can be used to determine the number of sources of pollutant sources, the chemical composition of each source, and the amount that each source contributes to each sample, based on the ambient chemical composition data without requiring source characterization data.

Source Profiles

A significant advantage of the multivariate techniques such as PMF is that the additional data required to provide statistically significant trends for source resolution allows the correlations in ambient data to define the chemical composition of individual sources.  With knowledge of the chemical composition of common sources, these profiles can be assigned to specific source categories and these profiles can be used as an external verification of the source attribution process.

Eight representative sources were determined for fine PM (PM2.5) collected in project year 2 at two East Texas monitoring sites: Dallas (urban) and San Augustine (rural).  The chemical composition resolved for isolated factors from Dallas is shown in Figure 1.  The resolved source factors were very similar for San Augustine and are not shown.  The sources have been identified with labels based on the chemical composition isolated from the ambient data.

Source Contributions

Using the chemical composition analysis performed on samples collected between November 2005 and April 2006, source contributions to fine particulate matter levels were determined.  As shown in Figure 2, the relative contributions of secondary sulfate and motor vehicle emissions were similar between the two sites, the contribution of wood smoke and soil sources was greater at the rural site (San Augustine), and the contribution from meat cooking operations was greater at the urban site (Dallas).

Figure 1: Source profiles isolated based on the ambient field data collected in Dallas, TX between November 2005 and April 2006.

Figure 2: Relative source contributions as determined by positive matrix factorization of data collected at two sites in Texas.

Future Activities:

After moving to Arizona State University, our research group initiated a field campaign in Higley, AZ.  This community, on the fringe of the Phoenix metropolitan area, is undergoing transformation from a primarily agricultural to a suburban locale.   The local community experiences high coarse particulate matter levels from a variety of sources including agricultural operations, construction of new housing and traditional urban sources.  Sampling commenced in January 2008 and includes fine particulate matter, coarse particulate matter and local soils.

Journal Articles:

No journal articles submitted with this report: View all 21 publications for this project

Supplemental Keywords:

agricultural emissions, source apportionment, molecular markers, soil carbohydrates, fine particulate matter, coarse particulate matter,, RFA, Scientific Discipline, Air, particulate matter, Environmental Chemistry, Environmental Monitoring, atmospheric particulate matter, chemical characteristics, airborne particulate matter, agricultural soils, molecular markers, PM, fine particulate formation

Progress and Final Reports:

Original Abstract
  • 2005 Progress Report
  • 2006 Progress Report
  • 2008 Progress Report
  • 2009
  • Final Report