See more from this Session: Symposium--Minerals, Nanoparticles, and Health: I
Tuesday, October 18, 2011: 11:00 AM
Henry Gonzalez Convention Center, Room 212B, Concourse Level
Exposure to particulate matter is associated with an increased risk of cardiovascular and respiratory morbidity, asthma, lung cancer, inflammation and increased mortality. In the USA, the largest single source of both PM10 and PM2.5 is road dust. For over 40 years, the Nellis Dunes Recreation Area (NDRA) in southern NV has been heavily used for off-road vehicle (ORV) recreation with an estimated 300,000+ drivers per year. Our research shows that wind erosion is greatest in the sandy areas and ORV emissions are greatest in the silty and rock-covered areas. ORV emissions increase exponentially with driving speed, and are highest for 4-wheelers. On an annual basis, ORV-generated emissions equal natural dust emissions. Extremely high concentrations of naturally occurring arsenic are present. To our knowledge, no previous study has reported As concentrations in airborne dust from natural surfaces as high as those found in this study: PM10 up to 290 ppm; PM60 up to 312 ppm. Water-soluble arsenic is as high as 14.7 ppm. The dust also contains palygorskite, which commonly crystallizes in an asbestiform morphology. The International Agency for Research on Cancer has concluded that palygorskite fibers greater than 5 μm in length are possibly carcinogenic to humans. Emission rates for arsenic were calculated for all surface types in NDRA. Sandy areas have the potential to emit the greatest amount of arsenic-containing dust during windy conditions, whereas specific silt, rock-covered, and silty sand units have the highest arsenic emissions during ORV activities. In vivo experiments were conducted in mice to examine the immunotoxicological and histopathological effects following 3 daily exposures to dust samples from 3 surface types. Suppression of humoral immunity and splenic T-lymphocytes were the most sensitive parameters affected. Toxicology and human exposure data will be collected to define site-specific parameters for probabilistic modeling of human health risks.