Impact/Purpose:

To deliver improved, documented, and tested receptor models for use by State and local air pollution staff, as tools for SIP development.

Description:

Source apportionment (receptor) models are mathematical procedures for identifying and quantifying the sources of ambient air pollutants and their effects at a site (the receptor), primarily on the basis of species concentration measurements at the receptor, and generally without need of emissions and meteorological data. NERL's particulate matter (PM) source apportionment program covers both measurements and modeling. This task is concerned with the development of receptor models and applications of these models to ambient and human exposure PM data. Models of current interest are Chemical Mass Balance (CMB), the second generation multivariate models Unmix and Positive Matrix Factorization (PMF), and the Quantitative Trajectory Bias Analysis (QTBA) hybrid back-trajectory model.

Record Details:

Record Type:PROJECT

Start Date:10/01/2002

Projected Completion Date:09/01/2009

Record ID: 56100

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Project Information:

Progress :In FY03, development and applications work was initiated simultaneously on four different models.

In FY04, Improvements were added to EPA Unmix 2.3 and a beta version of EPA Unmix 3.0 was developed. This version of the software greatly increased the program speed and added partial solutions, estimate total mass of species not in the initial solution, improved algorithm for estimating source profile uncertainties, replace missing data, and automatic selection of species. A contract was awarded in FY04 to develop algorithms for the selection of species and observations. In addition, the FY04 contract included development of code to allow Unmix to without user intervention "Automatic Unmix." This should finish the development cycle for EPA Unmix, however, some additional funding may be required in FY05 to address reviewers comments.

In FY04, the development of EPA PMF 1.0 was initiated and a beta version developed. Formally, the model being used is the Multilinear Engine (ME-2). EPA PMF 1.0 includes pre-processing diagnostics, menus to easily execute the model, visualization of resulting profiles and contributions, and diagnostics, including uncertainty algorithms, for evaluating the robustness of the solution. In FY05, EPA PMF 1.0 will be enhanced to include auxiliary information (such as wind direction, wind speed, weekend/weekday) to better resolve and identify sources. Additional pre- and post-processing tools will also be added. Under contract to Clarkson University, PMF is being applied to the RTP PM exposure data.

In FY04, additional improvements were added to the CMB model code and a contract was developed for external review of the software and user guide. The development of future versions of CMB will be supported by OAR. In addition, a CMB application project was initiated and the Northern Front Range Air Quality Study (NFRAQS) data were obtained from Desert Research Institute. The NFRAQS data contains both conventional (XRF, OC/EC, NO3) and extended species (organic PM speciation). A work assignment was initiated for the analysis of the NFRAQS data with EPA Unmix, PMF, and CMB by ManTech.

A contract with the University of Michigan to develop an improved version of the Quantitative Trajectory Bias Analysis hybrid back trajectory receptor model is in final negotiations and the contract should be awarded by the end of November 2004.

A contract was awarded to Clarkson University for the receptor modeling of the RTP PM exposure data with PMF. The data sets for the receptor modeling analysis were developed by EPA and provided to Clarkson, and they are currently waiting for organic and elemental carbon blank data from EPA.

Relevance :The availability of tested receptor models for use in SIP development in the 2005 - 2008 time frame is a recognized critical need. Three candidate receptor models are available, but are not in a form that can be used easily and with confidence by State and local air pollution staff. Necessary evaluation through actual application to typical ambient data sets has been limited, and has suggested the need for improvements not only in the underlying mathematical algorithms but in the models' ease of use as well. This task is needed to perform the improvement and evaluation steps for each of the three models in a timely manner for making the models available for SIP development and evaluation of the effectiveness of implemented emission control strategies



Clients :OAQPS, EPA Regions, State & Local Agencies, Regional Planning Organizations

Project IDs:

ID Code :15099

Project type :OMIS