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JOHNS HOPKINS PARTICULATE MATTER RESEARCH CENTER

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Impact/Purpose:

The Johns Hopkins PM Research Center brings together a multidisciplinary research team: biostatisticians, epidemiologists, exposure assessors, lung biologists and respiratory toxicologists, pulmonary clinicians, and atmospheric scientists to address the most critical gap in current understanding of health and particulate matter (PM) the physical and chemical characteristics that determine risk to human health. The Center's conceptual foundation lies in mapping health risks of PM across the US, based on analysis of national databases on air pollution, mortality, and hospitalization, and then using the maps to guide detailed monitoring and collection of PM samples for physical, chemical, and biological characterization in assays relevant to pulmonary and cardiovascular outcomes.

Description:

Project 1
Project 1 has completed much of its proposed work, having previously characterized the variation in the PM-associated risk for hospitalization and mortality across the country and also analyzed the available data from the STN for the years 2000-2005.  These analyses were critical to the selection of locations for PM collection and monitoring Project 2.  Briefly, analysis of PM mass across the 203 counties indicated variability in outcomes was largely accounted for by seven components, suggesting that the 52 components assessed by the STN, some tightly co-varying spatially and temporally, share sources.  The seven were sulfate, nitrate, silicon, elemental carbon (EC), organic carbon matter (OCM), sodium ion, and ammonium ion. These seven components, in aggregate, constituted 83% of the total PM2.5 mass, whereas all other components individually contributed less than one percent.  Ambient levels of EC and OCM, which are generated primarily from vehicle emissions, diesel, and wood burning, were found to be associated with the largest risks of emergency hospitalization across the major chemical constituents of PM2.5.
 
During the past year Project 1 investigators focused efforts on a number of methodological issues. These include accounting for exposure measurement error in estimating acute health effects of coarse particulate matter; imputing missing PM data; and proposing a new study design and a statistical model, which uses spatio-temporal information to estimate the long-term effects of air pollution exposure on life expectancy.  Highlights of this work are described below.
 
Project 1 has developed an innovative statistical methodology for imputing missing PM2.5 data when PM10 data from the co-located monitor are available (and vice-versa).  We assume that the two daily time series of PM10 and PM2.5 from the pair of collocated monitors form a data cluster.  The goal is to best predict the missing PM data at the co-located monitor pair in presence of multiple competing prediction models.
New statistical models that account for the spatial variability in PM2.5 components when used in community-level time series models of PM2.5 components and health outcomes were developed. The new methodology uses spatial-temporal statistical modeling to adjust for the uncertainty in estimating ambient average levels of PM2.5 components in health risk models. In addition to developing new statistical methodology, Project 1 also published a comprehensive characterization of community-level spatial variation in PM2.5 components across the U.S. This work identified a number of factors that contribute the spatial variation in PM2.5 components, including distance and season.
 
Project 1 also developed a new method, called Bayesian Adjustment for Confounding (BAC).  BAC can be used to estimate an exposure effect while accounting for the confounders, as well as the uncertainty about which of the confounders should be considered.
 
Finally, Project 1 developed a corrected measure of the treatment effect adjusted for confounding that does not depend upon the non-linearity effect.  The performances of the simple and corrected estimates of confounding were assessed in simulations, and illustrated using a study of risk factors for low birth weight infants.  The simple estimate of confounding was found to be adequate or even preferred in settings where the non-linearity effect is very small.  In settings with a sizable non-linearity effect, the corrected estimate of confounding has improved performance.
 
Project 2
Project 2 has completed monitoring and bulk sample collection in all planned locations:  King, WA; Sacramento, CA; Maricopa, AZ; Hennepin, MN; Harris, TX; Pinellas,  FL; and Jefferson, KY; Allegheny, PA; and Queens, NY.  This intensive field work included setting up and managing monitoring in the final 3 locations (Jefferson County, KY; Allegheny County, PA; and Queens, County, NY) during the past year. Coarse and fine bulk PM samples from all locations visited have been analyzed for anions, 24 metals, and elemental carbon (PM2.5 only), and PAHs.
 
All bulk and filter PM samples have been analyzed for platinum group elements, and all bulk fine PM has been analyzed by XANES/EXFAS at the National Synchontron Light Source for oxidation states of manganese, iron, and chromium.
Project 2 is also using publicly available emissions data to generate maps of sources that potentially account for the PM variability found in each monitoring location.  In addition, we are conducting chemical mass balance analysis using source profiles from EPA’s Speciation database and data collected by us at each site.  The purpose of these analyses is to provide an exploration of the source of PM components found at the different locations.  Finally, bulk fine PM from all locations visited to date has been delivered to Project 3 for toxicity assessment.
 
Project 2 team has also been interacting with investigators from Project1 to apply principal components analysis (PCA) to the PM composition data collected in Project 2 in order to develop "source-like" indicators of the overall PM mixture. These results will be used to explain differences in toxicity being explored by Project 3.  Because the number of samples is small, the use of dimension reduction techniques such as PCA is critical so that the number of predictors does not exceed the number of observations.
 
Project 3
Project 3 investigators have continued their investigation of PM toxicity with bulk PM collected by Project 2 using in vitro and in vivo models.  Results from experiments conducted last year are highlighted below.
Project 3 investigated the protective role of H2S, an endogenous gaseous molecule in circulation on PM-induced endothelial barrier disruption and pulmonary inflammation. Changes in endothelial monolayer permeability reflected by Transendothelial Electrical Resistance (TER), reactive oxygen species (ROS) generation, and murine pulmonary inflammatory responses were studied after exposure to PM and NaSH, a H2S donor. These studies suggest a protective role of H2S in PM-induced endothelial disruption, pulmonary inflammation and remodeling.
 
Using PM provided by Project 2, the Project 3 investigators found some interesting preliminary differences in toxicity.  For example, fine-PM from Maricopa induced a much higher level of AHR, and infiltration of white blood cells, including neutrophils and eosinophils into the alveolar space compared to PM collected form Sacramento.  Maricopa PM also exhibited a significantly higher level of AHR, and infiltration of white blood cell count, including neutrophils and eosinophils.
 
Using a murine model of cardiomyopathy, Project 3 has demonstrated that PM from both Maricopa and Sacramento resulted in a reduction in heart rate variability, respiratory dysynchrony, and increased frequency of serious ventricular arrhythmias. 

Record Details:

Record Type:PROJECT( ABSTRACT )
Start Date:10/01/2005
Completion Date:09/30/2010
Record ID: 144547