EVALUATION OF THE CARCINOGENIC RISK OF BIOCHEMICALLY INERT INSOLUBLE PARTICLES BY THE EPAA USING RAT INHALATION DATA

Authors
Citation
We. Pepelko, EVALUATION OF THE CARCINOGENIC RISK OF BIOCHEMICALLY INERT INSOLUBLE PARTICLES BY THE EPAA USING RAT INHALATION DATA, Particulate science and technology, 14(2), 1996, pp. 123-134
Citations number
36
Categorie Soggetti
Engineering, Chemical
ISSN journal
02726351
Volume
14
Issue
2
Year of publication
1996
Pages
123 - 134
Database
ISI
SICI code
0272-6351(1996)14:2<123:EOTCRO>2.0.ZU;2-I
Abstract
Regulations pertaining to inhalable particulate matter are promulgated primarily by three program offices of the U.S. Environmental Protecti on Agency (EPA): Pollution Prevention and Toxic Substances (OPPTS), Ai r Quality Planning and Standards (OAQPS), and Mobile Sources (OMS). Ri sk assessment for these agents are carried out either by the program o ffices or by the National Center for Environmental Assessment (NCEA) f ormerly the Office of Health and Environmental Assessment (OHEA). Part iculate matter pollutants within the regulatory domain of OAQPS for wh ich either quantitative or qualitative assessment of cancer risk has b een carried out include asbestos, beryllium, cadmium, nickel refinery dust nickel subsulfide, and ambient particulate matter of less than 10 mu m diameter (PM10). OPPTS has qualitatively evaluated manmade miner al fibers, titanium dioxide, and vermiculite. Asbestos is the only fib er for which cancer quantitation has been carried out. For several of these agents, risk is based upon human data with animal studies provid ing supporting data. Both qualitative and quantitative assessment of c ancer risk from exposure to diesel engine emissions is under developme nt by NCEA for OMS. Quantitative assessment of cancer risk from exposu re to this agent is described san example of EPA's approach to the use oi rats for evaluation of cancer risk. The major uncertainties relati ng to this assessment include the appropriateness of rat data for asse ssing human risk and the selection of a low-dose extrapolation model.