MPH 506 Week 4 Risk Assessment Write-Up Example

Risk Assessment Write-Up: Lead From Leaded Aviation Fuel at Harmon Field and Children Under Six in Eastgate, With Every Parameter Sourced and Every Estimate Labeled

Student Name

Chamberlain University

MPH 506: Environmental Health in Public Health

Instructor Name

Month Day, Year

Risk Assessment Write-Up: Lead From Leaded Aviation Fuel at Harmon Field and Children Under Six in Eastgate, With Every Parameter Sourced and Every Estimate Labeled

Scope and Question

This write-up assesses the added risk to children under six in the Eastgate neighborhood from lead emitted by piston-engine aircraft at Harmon Field, the airfield the county owns. It works through the familiar four steps, from identifying the hazard through characterizing the risk, and keeps each one apart so that an assumption made in one step can be traced. The question is narrow: how much does the airfield add to these children's lead intake and blood lead, and how confident can the county be in the answer? Lead from old paint and other sources is treated as background, not assessed here.

Step 1: Hazard Identification

Lead causes harm to the developing nervous system, and the evidence that it does so at low levels is strong and consistent across cohorts in several countries (Lanphear et al., 2005). The particular question for this step is whether lead from aviation fuel reaches children in a form and amount that matters. Two epidemiologic studies support that it does. In North Carolina, children within 500 meters of airports where leaded avgas was used had higher blood lead than other children, after accounting for housing age and other factors (Miranda et al., 2011). At a California airfield, children's levels tracked how close and how far downwind they lived and how busy the field was, and they eased when flights fell off (Zahran et al., 2023). The federal endangerment determination of 2023 drew on this same evidence (U.S. Environmental Protection Agency, 2023). The hazard is identified.

Step 2: Dose-Response, With Its Model

No threshold is assumed. This is a deliberate modeling choice, and it rests on the evidence rather than on caution alone: in the pooled analysis of seven cohorts, the IQ decrement per unit of blood lead was largest at the lowest levels, so that the first rise, from 2.4 up to 10 micrograms per deciliter, went with about 3.9 fewer IQ points (Lanphear et al., 2005). That relationship is nonlinear, steeper at the bottom, so a linear slope fitted across the whole range would understate the effect of small increases at low levels. For this assessment, the relationship is used only in the low range, where it implies roughly half an IQ point per microgram per deciliter. CDC's current marker for follow-up, 3.5 micrograms per deciliter, serves here only as a way of counting children with higher exposure than most, not as a threshold of safety (Ruckart et al., 2021).

Step 3: Exposure Assessment

Exposure is estimated for soil and dust ingestion, the main route for young children, with each parameter named and sourced.

Table 1

Exposure Parameters, Their Sources and Their Types

Note. Note. Soil values are composite figures constructed for this course; national defaults are used where no local data exist.

Using the central ingestion rate from the handbook (U.S. Environmental Protection Agency, 2017), a child in a near yard takes in about 11 micrograms of lead a day from soil and dust, compared with about 5 micrograms for a child in a far yard; the difference, about 6 micrograms a day, is the airfield's share if the near-far gradient is caused by aircraft. Using the upper ingestion rate, the difference rises to about 16 micrograms a day. These are intake estimates, not absorbed doses, and they do not include indoor dust that may be higher or lower than yard soil.

The attribution assumption is the weakest link. Near yards also sit in the oldest housing, so some of the gradient may come from paint rather than aircraft. To avoid stacking one uncertain intake model on another, this write-up does not convert intake to blood lead with a biokinetic model. It uses the empirical increment from Zahran et al. (2023), 0.72 micrograms per deciliter under peak traffic, as the best available estimate of the blood lead effect of a busy piston airfield on nearby children.

Step 4: Risk Characterization

The central estimate is that living close to the run-up pad adds in the range of half to three quarters of a microgram per deciliter to a young child's blood lead during busy periods, which, using the low-range slope, corresponds to a loss of a fraction of an IQ point per child. That is small for one child. Across the roughly 240 children under six in Eastgate, it shifts the distribution upward, which means more children cross the 3.5 reference value and the community's average cognitive potential falls slightly. Effects of this kind matter because they are borne by a whole population and cannot be reversed.

This estimate is central, not conservative. A conservative estimate would use the upper ingestion rate, assume all of the near-far gradient comes from aircraft and allow for higher indoor dust, which would roughly double the intake difference. The truth may also be lower, if paint explains much of the gradient.

Uncertainty

Four uncertainties dominate. Attribution between aircraft and paint is unknown. The county has one air monitor, at the fence, and none inside the neighborhood. The blood lead increment comes from another airport with different traffic and wind. And blood lead testing of Eastgate children reached fewer than half of those eligible in 2025, so the local distribution is not known.

What Would Reduce the Uncertainty

Three steps would narrow the range most for the least cost: stable isotope analysis of a sample of near-yard soils and indoor dust wipes, to separate aircraft lead from paint; a second air monitor inside Eastgate for a full year; and a blood lead testing drive for children under six, with results mapped by distance from the pad. Until those results are in, the central estimate is reasonable for planning, and it supports low-cost steps such as relocating the run-up area away from homes, which reduce exposure whatever the exact share turns out to be.

References

Lanphear, B. P., Hornung, R., Khoury, J., Yolton, K., Baghurst, P., Bellinger, D. C., Canfield, R. L., Dietrich, K. N., Bornschein, R., Greene, T., Rothenberg, S. J., Needleman, H. L., Schnaas, L., Wasserman, G., Graziano, J., & Roberts, R. (2005). Low-level environmental lead exposure and children's intellectual function: An international pooled analysis. Environmental Health Perspectives, 113(7), 894-899. https://doi.org/10.1289/ehp.7688

Miranda, M. L., Anthopolos, R., & Hastings, D. (2011). A geospatial analysis of the effects of aviation gasoline on childhood blood lead levels. Environmental Health Perspectives, 119(10), 1513-1516. https://doi.org/10.1289/ehp.1003231

Ruckart, P. Z., Jones, R. L., Courtney, J. G., LeBlanc, T. T., Jackson, W., Karwowski, M. P., Cheng, P.-Y., Allwood, P., Svendsen, E. R., & Breysse, P. N. (2021). Update of the blood lead reference value: United States, 2021. MMWR Morbidity and Mortality Weekly Report, 70(43), 1509-1512. https://doi.org/10.15585/mmwr.mm7043a4

U.S. Environmental Protection Agency. (2017). Update for chapter 5 of the exposure factors handbook: Soil and dust ingestion (EPA/600/R-17/384F). https://www.epa.gov/sites/default/files/2018-01/documents/efh-chapter05_2017.pdf

U.S. Environmental Protection Agency. (2023). Finding that lead emissions from aircraft engines that operate on leaded fuel cause or contribute to air pollution that may reasonably be anticipated to endanger public health and welfare (Final rule, 88 FR 72372). https://www.federalregister.gov/documents/2023/10/20/2023-23247/finding-that-lead-emissions-from-aircraft-engines-that-operate-on-leaded-fuel-cause-or-contribute-to

Zahran, S., Keyes, C., & Lanphear, B. (2023). Leaded aviation gasoline exposure risk and child blood lead levels. PNAS Nexus, 2(1), Article pgac285. https://doi.org/10.1093/pnasnexus/pgac285