Frequent Questions

How did EPA assess the level of protection that the Hazardous Waste Combustion Emission Standards Rule will provide?

EPA performed a comprehensive multipathway assessment of human health risk and a screening assessment of ecological risk.
Facility Selection
We modeled emissions from 76 facilities out of a universe of 172 facilities based on a stratified random sample. We chose the sample sizes within a given category (e.g., commercial incinerators, cement kilns) to ensure that the probability of modeling a facility in the upper ten percent of the distribution of risk would be 90 percent or greater. The probabilities actually achieved range from 88 to 100 percent.
Pollutants Evaluated
We made national emissions estimates for all chemical constituents covered by the rule for which sufficient data were available, including all 2,3,7,8-chlorine substituted dibenzo(p)dioxins and dibenzofurans, elemental mercury (Hg0), divalent mercury (Hg+2), lead, cadmium, arsenic, beryllium, trivalent chromium (Cr+3), hexavalent chromium (Cr+6), chlorine, and hydrogen chloride. In addition, emissions estimates were made for particulate matter (PM10 and PM2.5) and nine other metals. From these estimates of current emissions, we projected what the emissions would be under the MACT standards. We could not make chemical-specific national emissions estimates for organic constituents other than dioxins and furans (e.g., various products of incomplete combustion) due to the lack of sufficient emission measurements. 

Exposure Factors
We obtained data collected from previously published studies and used the data to derive exposure factor information, including information for children. In particular, we analyzed data collected by the United States Department of Agriculture (USDA) to estimate consumption of home-produced foods, such as meat, milk, poultry, and eggs. For fish, we used data from published studies of fish consumption by freshwater anglers and studies of Native Americans who consume significant amounts of freshwater fish.
Receptor Populations
We focused the assessment on non-subsistence receptor populations such as commercial farmers, recreational anglers, and non-farm residents whose numbers and locations can be estimated from available census data. However, we also considered subsistence activities such as farming and fishing.
Human Exposure and Risk
 We conducted a multi-pathway exposure analysis for all the human receptors in the area surrounding modeled facilities out to a distance of 20 kilometers (or about 12 miles). All persons residing within the study area were included in the analysis (with the exception of commercial poultry farmers who are likely to have much lower exposures than other farmers). We divided the study area into sixteen (16) sectors. For each sector, we used census data to estimate the population of those persons living in farm households by type of farm and the population of those persons living in non-farm households. We also used census data to determine the age of all household members. We considered four age groups: preschoolers (0 to 5 years), preteens (6 to 11 years), adolescents (12 to 19 years) and adults (20 years and older). 

We performed air dispersion and deposition modeling for each study area at all sample facilities using facility-specific information on stack configuration and emissions, along with site-specific meteorological data, terrain data (in areas of elevated terrain), and land use data. 

We performed exposure modeling using central tendency exposure factors (e.g., duration of exposure and daily food intake) for all receptor populations. In addition, for key exposure pathways and receptors, we performed an exposure factor variability analysis that considered the variation between individuals in the duration of exposure and food consumption rates. This analysis was done for farmers exposed to dioxins from home-produced beef or milk and for freshwater anglers exposed to methyl mercury from recreationally-caught fish. 

We estimated the risk of developing cancer from the estimated lifetime average daily dose and the slope of the dose-response curve. A cancer slope factor is derived from either human or animal data and is taken as the upper bound on the slope of the dose-response curve in the low-dose region, generally assumed to be linear, expressed as a lifetime excess cancer risk per unit exposure.
To characterize the potential risk of non-cancer effects, we compared the average daily dose (reflecting less than lifetime exposure) to a reference dose and expressed the result as a ratio or hazard quotient. The reference dose is an estimate of a daily exposure to the human population, including sensitive subgroups, that is likely to be without an appreciable risk of deleterious effects during a lifetime. The hazard quotient, by indicating how close the average daily dose is to the reference dose, is a relative measure of risk. 

We have not developed a reference dose for dioxins or lead. Therefore, we took a different approach for assessing the potential risk of non-cancer effects from these contaminants. For dioxins, we used a modified margin of exposure approach that compared the average daily dose to background exposures in the general population. As a measure of risk, the margin of exposure presupposes that if exposures are small relative to background, then they are likely to have limited significance for human health. 

For lead, we modeled the blood lead levels in young children that would result from exposures to lead in the diet, in ambient air, and in soil and dust and compared those levels to the Centers for Disease Control and Prevention guidelines on blood lead.
In addition, we assessed the reduction in the incidence of respiratory disease, cardiovascular disease, and premature mortality from reductions in fine particulate matter emissions (PM2.5 and PM10). 

Ecological Assessment
We performed a screening-level ecological assessment to assess the relative potential for adverse ecological effects. The analysis was based on media-specific ecological criteria thought to be protective of a range of ecological receptors. We compared modeled surface water concentrations to water quality criteria protective of aquatic life, such as algae, fish, and aquatic invertebrates, as well as piscivorous wildlife. Similarly, we compared modeled soil concentrations to soil criteria protective of the terrestrial soil community, as well as terrestrial plants and mammalian and avian wildlife. We also compared modeled sediment concentrations to sediment criteria protective of the benthic aquatic community.

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