Air Quality Core Overview

Investigators: Ted Russell, Ph.D. (PI, Georgia Tech), James Mulholland, Ph.D. (Georgia Tech), Yang Liu, Ph.D. (Emory), Talat Odman, Ph.D. (Georgia Tech), Yongtao Hu, Ph.D. (Georgia Tech), Guangxuan Zhu, Ph.D. (QA Advisor, Georgia Tech)

The primary mission of the Air Quality Core (AQC) is to provide Center researchers the information and methods to comprehensively characterize air pollutant mixtures relevant to their projects and to support project activities by collecting and managing data, developing a “Mixture Characterization Toolkit” (MC Toolkit) for further analyses specific to the projects, and providing the expertise and resources to facilitate the application of toolkit components. An additional mission of the AQC is to facilitate transmission of atmospheric data and methods to potential users outside of the Center.

The more comprehensive characterization will be developed, first, by analyses of the detailed chemical and physical measurements produced by the Center along with those available from other routine and special studies. Further spatial and temporal characterization of the air pollutant mixtures, and the sources involved, will come from the use of extended receptor-oriented and chemical transport models (CTM) applied over multiple scales. As envisioned, the MC Toolkit will include a range of both source- and receptor-oriented air quality models, regression approaches, and hybrid methods. Data collection and management will take advantage of the expertise and facilities previously developed by AQC personnel. These activities are efficiently conducted by the AQC team that has the resources and prior expertise with the collection, management and analysis of atmospheric data.

The AQC has six functions (described below) in support of the four research projects and other cores within the Center. In addition to these functions, the AQC team assists in the preparation of reports and journal publications resulting from Center activities.

Function 1. Atmospheric data collection and management.  Atmospheric data from Projects 1 and 2, along with routine, satellite and other special study observations will be collected for general use by Center projects and outside and for integrated analyses by AQC researchers as outlined in Functions 2-6. Support of Projects 1, 2, 3, 4.
Function 2. Development of the MC Toolkit to support Center projects. MC Toolkit will include processed and analyzed atmospheric data, analysis methods and air quality models for use by project teams. As part of the toolkit development, technique capabilities will be extended with particular focus on unique trace components to make use of the comprehensive ambient measurements. Components of MC Toolkit will include:
  • Data analysis products, descriptive assessments of observations, statistical analysis of individual pollutants, pollutant and meteorological data visualizations; in-situ, satellite and model products
  • Receptor-oriented data analysis and source apportionment methods, including regression approaches and receptor source apportionment (CMB, PMF, UNMIX)
  • Source-oriented air quality models, including CMAQ chemical transport model and Mobile Matrix-CALINE Grid
  • Hybrid receptor and source-oriented source apportionment model providing mixture characterization
Support of all Projects 1, 2, 3, 4.
Function 3. Support of health project teams using MC Toolkit for data analysis, model applications and interpretation of results, in conjunction with Biostatistics Core. Support of Projects 2, 3, 4. 
Function 4. Application of the extended Models 3/CMAQ to provide a comprehensive characterization of pollutant concentrations, sources and variations. CMAQ will be applied over a domain covering the continental US using finer spatial resolution over project study areas. The large scale application will provide a comparison of air pollutant mixtures across regions. Finer scale nesting within the project areas will inform project personnel as to the spatial, temporal and chemical variations that are not directly available from observations. Products of this Function will be added to MC Toolkit in support of Projects 1, 2, 3, 4.
Function 5. Integration of satellite remote sensing into health studies and air pollutant mixture characterization.  Satellite retrieved aerosol optical and microphysical properties and biomass burning parameters will be used with land use data to characterize the spatial patterns of particulate properties as well as the spatial impacts of wildfires and prescribed burns. Processed data will be added to the MC Toolkit. Support of Project 3. 
Function 6. Assessment of exposure misclassification across scales and source regions. Included in this task will be the use of detailed information of pollutant concentrations at multiple temporal and spatial scales from Project 1 to assess exposure error when considering populations within and across scales. Support of Projects 3, 4.