Grantee Research Project Results
2006 Progress Report: New Technologies for Source Apportionment
EPA Grant Number: R832157Title: New Technologies for Source Apportionment
Investigators: Henry, Ronald C.
Current Investigators: Henry, Ronald C. , Fine, Philip M.
Institution: University of Southern California
EPA Project Officer: Chung, Serena
Project Period: January 20, 2005 through January 19, 2008 (Extended to January 19, 2009)
Project Period Covered by this Report: January 20, 2006 through January 19, 2007
Project Amount: $450,000
RFA: Source Apportionment of Particulate Matter (2004) RFA Text | Recipients Lists
Research Category: Air , Air Quality and Air Toxics , Particulate Matter
Objective:
The project will develop new receptor-oriented methods of source apportionment based on the source information in wind speed and direction and the periodic variations of concentrations as extracted from the data by nonparametric regression and Fourier analysis. These methods will be applied to airborne particulate and other air quality data sets nationwide. Also, the source apportionment aspects of a particulate health-effects study to be carried out in Long Beach, CA will be strengthened by additional analysis of particulate samples for organic and inorganic species funded by this project. The newly developed receptor models will be augmented with the chemical mass balance (CMB) model and the Unmix multivariate receptor model.
Progress Summary:
Federal, state, and local authorities routinely operate thousands of air quality monitors throughout the United States and its territories to determine if National Ambient Air Quality Standards are being met. Most of the measurements are reported as 1-hour averages. However, many of these monitors actually measure 1 or 2 minute average values of carbon monoxide, nitrogen oxides, sulfur dioxide, or airborne particulate matter less than 10 micrometers in diameter (PM10). In many cases, meteorological instruments measuring wind speed and direction are collocated with these monitors. This project has developed methodology that uses short time average air quality and wind data to locate and quantify the contribution of local sources of air pollution impacting a monitor using only measurements of a single pollutant and winds at a single site.
The methodology is called Nonparametric Trajectory Analysis (NTA) and uses short time average (1-minute) observations of pollutant concentrations along with back trajectories to estimate the average concentration at the receptor given that air has passed over a nearby area. The points on each back trajectory are associated with the pollutant concentration when the trajectory arrives at the monitor. The average value of the pollutant at the monitor given that air has passed near a geographical point on a grid is calculated by nonparametric regression of the pollutant concentrations over all the back trajectories passing near the point for the period of interest. The model also apportions the average pollutant concentrations to local sources in geographically distinct regions. An example of geographical source apportionment is shown in Table 1.
Table 1. Geographical Source Apportionment of Average Sulfur Dioxide at Long Beach May through July, 2005
Sector | Azimuth |
Percentage |
South – ports, refinery, power plant | 170 - 246 |
49.2 |
West – refineries | 246 – 290 |
21.5 |
Southeast – airport, marine sources | 90 – 170 |
19.1 |
North | 290 – 20 |
5.5 |
Northeast | 20 – 90 |
4.7 |
Another major goal of this project is the characterization of the periodic variations in air quality and other environmental data since the existence of weekly or monthly periodicities is usually associated with anthropogenic sources. However, because of missing data and irregular sampling, standard methods of harmonic analysis cannot be applied to most air quality or environmental data in general. Building on the previous years work, the Lomb version of the Fourier Transform was used to estimate the power and phase in the major periodicities in the data and the first and second harmonics. This information characterizes, for most environmental time series, the shape and timing of the periodic behavior. A method to automatically classify and group species by the periodic behavior has been developed.
As periodic behavior will vary over a long time series, the sliding window Fourier Transform has been applied for the first time to environmental time series. Previously, application of the Lomb periodogram to a 13 year time series of 24 hour elemental carbon data from Washington DC found a strong 7-day periodicity peaking at midweek and having a minimum on the weekend. The figure below shows the results of applying a sliding window Fourier Transform to this time series. The sliding window is 1 year wide and it slides by 30 days at each step. The figure shows that the power in the 7-day periodicity versus the date of the middle of the sliding window. The power in the 7-day periodicity varies greatly from year to year and completely disappears at times. This implies that major changes in the sources of elemental carbon affecting the monitor have occurred over the years. The presumed cause of this 7-day periodicity is weekly periodicity in heavy-duty diesel vehicle emissions; there are being fewer trucks on the road on weekends. Thus, the results seem to imply a change in the impact of diesel emissions over the years. Although, the changes in the power of 7-day periodicity could be caused by other sources of black carbon affecting the monitor. In any case, the sliding window Fourier Transform results imply major changes in the sources of elemental carbon near the monitor.
Future Activities:
Nonparametric Trajectory Analysis (NTA) will be extended to use back trajectories up to 12 hours long instead of the current 3-hour trajectories. The South Coast Air Quality Management District has made available to the project several years of 1-minute air quality and meteorological data from most of its monitoring sites. Analysis of this unique data will be the major task of the next year. Also, the NTA will be applied to black carbon data measured by light absorption over a period of minutes. The major objective of the organic analysis/CMB work is to determine the chemical composition of size-fractionated PM at various sites in Long Beach to evaluate PM components and their relationship to various PM sources on a community scale. This will be accomplished by quantifying individual organic compounds in size-fractionated samples from the various sampling locations; identifying previously studied source profiles for major sources categories in the Long Beach area; and using chemical mass balance methods to estimate contributions of different source types to the measured pollutant concentrations.
Journal Articles:
No journal articles submitted with this report: View all 15 publications for this projectSupplemental Keywords:
Air quality, source apportionment, chemical mass balance, organic aerosol, statistical analysis, nonparametric regression,, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Air Quality, Environmental Chemistry, Monitoring/Modeling, Environmental Monitoring, Environmental Engineering, particulate organic carbon, atmospheric dispersion models, atmospheric measurements, model-based analysis, source apportionment, chemical characteristics, emissions monitoring, environmental measurement, airborne particulate matter, air quality models, air quality model, air sampling, speciation, particulate matter mass, analytical chemistry, modeling studies, real-time monitoring, aerosol analyzers, chemical speciation sampling, particle size measurement, atmospheric chemistryProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.