A5051846879- Air Quality and Health Impacts
- Atmospheric chemistry and aerosols
- Air Quality Monitoring and Forecasting
- Vehicle emissions and performance
- Advanced Statistical Methods and Models
- Water Quality Monitoring and Analysis
- Urban Heat Island Mitigation
- Spectroscopy and Chemometric Analyses
- Atmospheric and Environmental Gas Dynamics
- Visual perception and processing mechanisms
- Impact of Light on Environment and Health
- Color perception and design
- Odor and Emission Control Technologies
- Advanced Chemical Sensor Technologies
- Water Quality and Resources Studies
- Concrete Corrosion and Durability
- Statistical Methods and Bayesian Inference
- Climate Change and Health Impacts
- Wind and Air Flow Studies
- Analytical Chemistry and Chromatography
- Astronomy and Astrophysical Research
- Scientific Measurement and Uncertainty Evaluation
- Color Science and Applications
- Groundwater and Isotope Geochemistry
- Smart Materials for Construction
University of Southern California
2006-2025
Space Telescope Science Institute
2000-2012
University of Wisconsin–Milwaukee
2007-2010
Environmental Protection Agency
2001-2009
Southern California University for Professional Studies
1993-2006
National Center for Environmental Assessment (EPA)
2005
Texas A&M University System
2001
University of Washington
2001
University of California System
1990
University of Minnesota Medical Center
1988
Abstract The multivariate receptor model Unmix has been used to analyze a 3-yr PM2.5 ambient aerosol data set collected in Phoenix, AZ, beginning 1995. analysis generated source profiles and overall average percentage contribution estimates (SCEs) for five categories: gasoline engines (33 ± 4%), diesel (16 2%), secondary SO4 2− (19 crustal/soil (22 vegetative burning (10 2%). was supplemented with scanning electron microscopy (SEM) of limited number filter samples information on possible...
Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 μm (PM2.5) and human mortality is well established, most responsible particle types/sources are not yet certain. In May 2003, U.S. Environmental Protection Agency's Particulate Matter Centers Program sponsored Workshop on Source Apportionment of PM Health Effects. The goal was evaluate consistency various source apportionment methods in assessing contributions daily PM2.5...
As described in this paper, nonparametric wind regression is a source-to-receptor source apportionment model that can be used to identify and quantify the impact of possible regions pollutants as defined by direction sectors. It detail with an example its application SO2 data from East St. Louis, IL. The uses kernel smoothing methods apportion observed average concentration pollutant sectors ranges speed. Formulas are given for uncertainty all important components model, these found give...
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTVehicle-Related Hydrocarbon Source Compositions from Ambient Data: The GRACE/SAFER MethodRonald C. Henry, Charles W. Lewis, and John F. CollinsCite this: Environ. Sci. Technol. 1994, 28, 5, 823–832Publication Date (Print):May 1, 1994Publication History Published online1 May 2002Published inissue 1 1994https://pubs.acs.org/doi/10.1021/es00054a013https://doi.org/10.1021/es00054a013research-articleACS PublicationsRequest reuse permissionsArticle...
Two easily available multivariate source apportionment models, Unmix and positive matrix factorization (PMF), often produce nearly the same apportionment. However, this paper gives two examples in which is not case: a simulated air pollution data set of 8 species 200 samples water quality 32 PCB congeners 106 sediment core from Sheboygan River Inner Harbor, WI. In first case, basic form PMF fails primarily because compositions do have any with zero or near concentrations. produces...