Community health perceptions and human environmental exposure to chromium contamination in a small New Jersey City

Following a 1983 chromic acid (hexavalent chromium [CrVI]) spill from a Garfield, NJ electroplating plant, CrVI-contaminated water was found in a local firehouse basement in 1993. An ATSDR public health advisory was issued for the plant site in 2010, and from 2008-2015, fourteen residential properties have required remediation to address CrVI-contaminated dust in the basements. As part of the Community Outreach and Engagement Core of the NYU NIEHS Center, seventytwo Garfield residents aged 18-65 years, participated in a community survey with the goal of identifying concerns related to environmental and community health. Thirty-two percent responded that they 'didn't know' if they were exposed to chemicals or pollutants where they live. This finding suggests a limited awareness of environmental chemical exposures, chromium contamination and/or potential exposure to CrVI. Furthermore, toenail clippings were collected from forty-seven Garfield residents and analyzed for total chromium levels to assess potential long-term exposure. On average, residents living on/inside the contaminated plume area had higher total chromium levels in their toenail clippings than residents living outside the plume area. However, chromium levels for all participants were within the range of historical normal. This study highlights the value of partnerships between environmentally-impacted community's and academic scientists working together to identify potential contaminant exposures and address public health concerns through research and environmental health education.

Binary classification by stochastic neural nets

We classify points in R(d) (feature vector space) by functions related to feedforward artificial neural networks. These functions, dubbed "stochastic neural nets", arise in a natural way from probabilistic as well as from statistical considerations. The probabilistic idea is to define a classifying bit locally by using the sign of a hidden state-dependent noisy linear function of the feature vector as a new (d+1)th coordinate of the vector. This (d+1)-dimensional distribution is approximated by a mixture distribution. The statistical idea is that the approximating mixtures, and hence the a posteriori class probability functions (stochastic neural nets) defined by them, can be conveniently trained either by maximum likelihood or by a Bayes criterion through the use of an appropriate expectation-maximization algorithm.

Methods development for epidemiologic investigations of the health effects of prolonged ozone exposure. Part III. An approach to retrospective estimation of lifetime ozone exposure using a questionnaire and ambient monitoring data (U.S. sites)

Methods are needed for retrospective estimation of long-term ozone exposures in epidemiologic studies. The overall objective of this study was to evaluate whether data from available U.S. ozone monitoring sites are useful for estimating lifetime ozone exposures of young adults (for example, college students). Several aspects of this question were evaluated. First, we applied and (compared several spatial interpolation methods to a set of long-term average ozone data from all U.S. monitoring sites in operation from 1981 through 1990. Interpolation methods included simple and weighted averages, linear regression, and, in an exploratory way, kriging. The comparison of methods was carried out for five different metrics of ozone concentration: the daily one-hour maximum (MAX1) and eight-hour maximum (MAX8), the average ozone concentrations between 10 a.m. and 6 p.m. (MID8) and between 10 a.m. and 10 p.m. (MID12), and the sum of all hourly ozone concentrations greater than or equal to 60 parts per billion (ppb) (SUM06). We also tested whether interpolations were improved by modeling the influence of covariates such as population density, elevation, and weather on ozone concentrations. We analyzed the reliability of a set of newly developed questions about past activity levels among a group of 52 freshmen students at Yale University. This was done by analyzing the agreement between answers to the same questionnaire administered two times, one month apart (test and retest), to the same students. Finally, we combined the interpolation models with residential history information obtained by questionnaire to derive long-term ozone exposure estimates for a group of 200 Yale freshmen. Results of our study showed that the density of available monitoring sites appears to be adequate for estimating spatial patterns of long-term average ambient ozone concentrations. A simple regression-based interpolation on the three nearest sites produced consistently good results. Including covariates in the interpolation models did not substantially improve the estimates. The largest estimation errors occurred for areas where ozone concentrations were highest. The newly developed activity history questions exhibited fair to moderate reliability, The results of this work imply that reasonably precise estimates of long-term ambient ozone concentrations for use in large-scale epidemiologic studies can be achieved by interpolating ozone concentrations between available U.S. monitoring sites. This study did not address the issues of whether and how retrospective data on factors that modify exposure or dose (e.g., indoor/outdoor penetration of ozone and time outdoors) can be used to derive estimates of long-term personal ozone exposures and contribute to the assessment of received dose.

The tar fraction of cigarette smoke does not promote arteriosclerotic plaque development

In addition to being the single greatest known environmental cause of cancer, cigarette smoke (CS) is also a major contributor to heart disease. We reported previously that 1) inhalation of either mainstream or sidestream CS promotes aortic arteriosclerotic plaque development; 2) 1,3 butadiene, a vapor-phase component of CS, promotes plaque development at 20 ppm, which at the time was only 2 times higher than the threshold limit value; and 3) individual tar fraction carcinogens in CS, including polynuclear aromatic hydrocarbons (PAHs) and nitrosamines, either do not promote plaque development or do so only at high concentrations. These results suggested that the tar fraction is not the primary source of plaque-promoting agents in CS. We asked whether repeated exposure to the tar fraction of CS, collected in a cold trap (TAR), promotes plaque development in an avian model of arteriosclerosis. Acetone extracts of mainstream CS tar from burning, unfiltered reference cigarettes were solubilized in dimethyl sulfoxide (DMSO) and injected weekly into cockerels for 16 weeks (25 mg/kg/week). Positive controls were injected weekly with the synthetic PAH carcinogen, 7,12 dimethylbenz(a)anthracene (DMBA) dissolved in DMSO and negative controls were injected with DMSO. Plaque location and prevalence did not differ from group to group. Morphometric analysis of plaque cross-sectional areas showed that plaque sizes, which are log-normally distributed, were significantly larger in the DMBA cockerels compared to both the TAR and DMSO groups. There were no significant differences in plaque size between DMSO and TAR cockerels. The results reported here, combined with other recent findings, support the conclusion that the primary arteriosclerotic plaque-promoting components of CS are in the vapor phase.