CHAPTER 5: Current Understanding of Airport-Related Air Quality and Health Impacts

This chapter provides the current state of research related to potential health impacts from airport pollutant emissions. It has been organized to respond to the following basic, key questions:

• What pollutants are of most concern at an airport?
• What are the airport contributions to local air quality and health impacts?

The answers to these questions were obtained through a preponderance of the existing research studies conducted in this area. The latter question is a combination of airport contributions to ambient pollutant concentration levels as well as potential health impacts (risks). With ongoing research in these areas, it should be noted that the answers are representative of a snapshot in time, and they may change with future research. Although there are some overlaps in the answers, they are kept to a minimum but are necessary to properly answer each question.

5.1 WHAT POLLUTANTS ARE OF MOST CONCERN AT AN AIRPORT?

5.1.1 Evaluations

At first glance, the answer to the question of which pollutants are of most concern may simply be based on what pollutants are emitted by the airport and their toxicities. But in order to answer this question, one must consider the risks associated with each pollutant. As previously explained, risk involves taking into account emissions and exposure in addition to toxicity. Just considering toxicity may cause undue attention to be paid to a pollutant that may be emitted in small quantities at an airport such that it may pose minimal risks to the public. In contrast, just focusing on pollutants with high emission rates overall (for the whole airport) may cause undue attention to pollutants with relatively low toxicity that may have little or no impact on the public health. In addition, the exposure pathway needs to be considered. If an airport is located in a region where the geography and meteorological patterns are such that most of the emitted pollutants tend to move away from populated areas, the risks associated with that airport may be lower than those associated an airport with lower levels of emissions but with dispersion and atmospheric chemistry conditions that are conducive to exposing larger portions of the public to these emissions.

As a result, it can be very difficult to determine risks in a general sense across all airports (or even a group of airports) since each has distinctly different characteristics (e.g., mixes of sources, airport layout, operations, etc.). Therefore, each airport needs to be assessed separately for each pollutant, and all of the aforementioned factors need to be taken into account.

That said, researchers still attempt to define risks in a general sense to provide helpful information that may be used as a screening-type starting point to help the aviation community

make better decisions regarding airport planning efforts and emissions mitigation measures. That is, the research results could help identify which pollutants and emission sources to target for such efforts to best utilize available resources, and also identify the most important areas for future research work.

A study conducted under the FAA’s Partnership for Air Transportation Noise & Emissions Reduction (PARTNER) Program also involved the development of a prioritized list of pollutants emitted from airport sources (Levy 2008). The study included assessments of emissions of criteria pollutants and HAPs but focused on PM_2.5, ozone, and a selected group of HAPs (formaldehyde, acetaldehyde, benzene, toluene, acrolein, etc.). This reduced pollutant focus was based on a screening analysis that determined that the excluded compounds pose significantly less risk. Also, for pollutants such as NO₂, the literature was considered inadequate to develop the required concentration–response functions for the required risk assessments, and preliminary evidence indicated a greater criteria pollutant health impact from PM_2.5 and ozone (EPA 2004 and 2005).

The study included emissions from three airports: Chicago O’Hare International Airport (ORD), Hartsfield-Atlanta International Airport (ATL), and T.F. Green Airport in Providence, Rhode Island (PVD). These airports were selected based on size, likely magnitude of impact, and location. Emissions inventories for each airport were prepared with the FAA’s EDMS/AEDT, and dispersion modeling was performed using AERMOD and CMAQ, the latter of which was used with different grid cell sizes.

For the main comparison work, an intake fraction was defined as a “unitless measure characterizing the total population exposure to a compound per unit emissions of that compound or its precursor.” This metric was used to represent population-based exposures, which correspond directly with health risks for pollutants with linear concentration–response functions, and it allowed for rapid comparisons among pollutants and airports. The intake fraction also allowed for rapid estimation of health risks, as it was beyond the scope of this screening-level analysis to conduct more detailed health risk modeling.

Tables 5-1 and 5-2 provide comparisons of the risks by pollutant for each airport studied. The risk values (deaths/year) indicate that PM_2.5 clearly dominate the overall risk and their impacts are magnitudes higher than the other pollutants. For example, the risks for ORD using concentrations estimated by the AERMOD dispersion model are as follows:

• Total fine particles: 15 deaths/year,
• Total HAPs (air toxics): 0.09 deaths/year, and
• Highest ranking HAP (Formaldehyde): 0.043 deaths/year.

Table 5-1. Population risk (deaths/year) for three airports using AERMOD (50-km radius).

Source: Levy et al. 2008

Table 5-2. Population risk (deaths/year) for three airports using CMAQ (12- and 36-km grids).

Source: Levy et al. 2008

These results are consistent with general EPA risk statistics that also show significantly higher risks posed by fine particles. Furthermore, the study was simplified (for comparison purposes) such that the HAPs risks are actually cancer risks with only a fraction of that corresponding to death. As such, the relative contribution of fine particles would be even higher in comparison. Non-cancer effects such as those from acrolein and various other pollutants were not considered as part of the prioritizations, because the available data were not amenable to quantification, although the researchers noted that ambient acrolein in the grid cells surrounding the three airports exceeded its RfC, implying potential health effects. This would imply that other HAPs with respiratory effects could also contribute to health effects following the non-cancer risk assessment approach used by EPA and others. Although these non-cancer effects were not included in the prioritizations of this study, these effects should not be discounted or minimized. The negative values for ozone risk in Table 5-2 are indicative of the nuances of ozone chemistry where increasing NO_x emissions can reduce ozone concentrations over an area.

As part of the study, the prioritized list of HAPs by risk was compared to rankings based on just emissions and emissions with toxicity (potency). As indicated in Table 5-3, formaldehyde is at the top of each list, but there are significant differences. For example, without taking into account toxicity or exposure, the emissions-based list shows acetaldehyde as second while the others have the pollutant in sixth place. This comparison helps to exemplify the need to include all aspects of risk so that the relative impacts of such pollutants are properly understood. The pollutants selected for this project represent those that have the greatest risks based on airport emission levels and toxicity.

Another study conducted under the PARTNER Program (Project 15) used a combination of CMAQ and the Environmental Benefits Mapping and Analysis Program (BenMAP) to study airport air quality impacts from 325 U.S. airports, focusing on the nonattainment areas (Ratliff et al. 2009). BenMAP uses health impact functions for criteria air pollutants to relate changes in air concentrations to a change in the incidence of a health endpoint. Only the impacts from PM and ozone were included in the study. Similar to the previous studies, the modeled results indicated that almost all of the health impacts were due to fine particles with about 160 cases of PM-related premature mortality per year. Health impacts such as chronic bronchitis, non-fatal heart attacks, respiratory and cardiovascular illness, also were associated with aircraft emissions.

Although health concerns are associated with each of the criteria pollutants, the greatest risks (i.e., cancer and morbidity) seem to be posed by PM and HAPs. Specifically, PM_2.5 appears to pose the greatest risk to human health—magnitudes higher than HAP species. Formaldehyde was ranked as the HAP species having the greatest risk.

5.1.2 Summaries and Conclusions

Studies such as those described above illustrate the need to conduct further research on more pollutants and at additional airports, but they indicate that, with regard to the potential for health impacts (risk), fine PM appears to pose the greatest risk. As such, much of the current research in airport air quality has focused on fine particles. Among criteria air pollutants, ozone also can contribute significantly to public health impacts, although it would have a lesser impact in the near field and has been excluded from some previous analyses given methodological limitations. For HAPs, formaldehyde was ranked as having the highest risk followed by others such as 1,3-butadiene, styrene, naphthalene, benzene, acetaldehyde, etc. Although fine particles may pose

much greater risk, it does not negate the need to further investigate other pollutants. In addition, although many previous analyses have focused on fine PM mortality given its large contribution to monetized health impacts, additional health outcomes from PM_2.5 and other pollutants merit inclusion.

5.2 WHAT ARE THE AIRPORT CONTRIBUTIONS TO LOCAL AIR QUALITY AND HEALTH IMPACTS?

The health effects of each pollutant are summarized in Chapter 4. Although there are uncertainties associated with the toxicities and exposures of these pollutants, the health effects are well documented. Organizations such as the EPA and the World Health Organization (WHO) provide extensive information on pollutant health effects.

• EPA Risk: https://www.epa.gov/risk
• WHO Risk: http://www.who.int/mediacentre/factsheets/fs313/en/

Although the overall airport emissions characteristics (mix of pollutants, chemical characteristics, sizes ranges for PM, etc.) may not be the same as other sources, the health effects of each pollutant are the same. That is, all other things being equal, exposure to an ambient concentration of a pollutant emitted from an airport for a specified period of time will produce the same health effects in an individual as the same exposure (i.e., concentration level and exposure time) to emissions from other sources (or another airport)—if the pollutants are identical (no differences in characteristics). As such, most studies that have addressed the question of airport impacts on local air quality and health impacts have used data from measurements or modeling results to provide indications of exposure (either with emissions or ambient pollutant concentrations) and have linked these data with literature-based concentration–response functions within human health risk assessments. These encompass correlating airport activities (e.g., aircraft operations) with emissions, modeling how those emissions influence concentrations, and comparing airport concentration contributions to background levels. Since no two airports are the same, it is difficult to make general statements regarding airport contributions to local air quality because this depends on many factors including emissions strength (emission factors and activity levels), airport layout, and local meteorology.

Researchers have attempted to determine whether airports have a discernible influence on local air quality. Some studies have indicated that pollutant concentration levels near an airport are similar to urban levels (e.g., Tesseraux 2004, McGulley 1995, and KM Chng 1999), which can result in a misunderstanding that airports overall have little or no impact on local air quality. Contrary to this, there have been several measurement studies that indicate that pollutant concentrations around airports are elevated (e.g., Wood 2008, RIDEM 2008, Zhu 2011). Airports have been found to contribute a small or negligible amount to local air quality for some criteria pollutants and HAPs species, but have been found to contribute a significant amount to local UFP

concentrations. Some modeling studies have quantified the concentration contributions of airports along with the associated health risks (e.g., Levy 2008, Sequeira 2008, and Barrett 2012, etc.).

Modeled estimates and measured findings for the specific contributions from airports to local air quality and health impacts are varied and depend on the pollutant. The focus of each study—which pollutants and health assessments were included, and which were left out—also is important. The following summaries provide examples of quantified airport contributions to ambient concentrations as well as health-related statistics.

5.2.1 Monitoring and Modeling of Airport Pollutants

5.2.1.1 Monitored Air Quality at Locations Near Airports

• Research by Hudda & Fruin (2016) and Keuken et al. (2015) showed UFP emissions were elevated at more than 8 km from airport runways. A study by Riley et al. measured UFP, PM, PM size distribution, NO₂, and BC near LAX, ATL, and freeways in the city (2016). Their findings agreed with previous studies showing elevated UFP levels below flight paths. Researchers noticed a distinction between PM size distribution near ATL and LAX versus near highways. Average particle diameters near the airport locations were smaller, closer to UFPs, and the PM size distribution was narrow. In contrast, measurements near roadway traffic and urban neighborhoods had larger average particle diameters and particle size distributions ranged from fine to coarse. This indicates that it may be possible to distinguish aircraft emissions from roadway emissions based on particle size and range. BC presence was higher in the roadway traffic emission measurements than in the airport emission measurements, which is expected given BC is emitted from diesel engines.
• Additional research by Hudda & Durant (2020) measured GHG, particulates (BC, PM_2.5, hydrocarbons), and UFPs in a typical residence downwind (~1 km) of the most used runway at Boston’s Logan International Airport. For comparison, measurements were taken near highway neighborhoods and data were obtained from five Massachusetts EPA monitoring sites in Boston. The study found meteorological conditions, particularly wind

direction which impacts where aircraft take off and land, played a major role in particle number concentrations (PNC) at the residence near the airport. Another finding was that high PNC counts were associated with high aircraft noise pollution. Evening hours between 5 and 11 p.m. had the highest PNCs at the airport as well as the residence. Residential indoor concentrations of NO_x and UFPs were comparable to or exceeded interstate concentrations.

• Five schools within a 7-mile radius of the Seattle-Tacoma International Airport had indoor air quality measurements taken before and after the introduction of a portable air cleaner with a HEPA rated filter. The study by Carmona et al. was particularly interested in UFP infiltration rate indoors (2022). Previous studies predicted outdoor to indoor infiltration between 10-70%. Factors such as building type, filtration systems, and building management influence this value. The outdoor air exchange rate (AER) was determined by increasing the indoor CO₂ concentration with dry ice and monitoring the rate at which the levels returned to ambient concentrations. The AER of the five schools ranged from 0.6/hr to 4.4/hr. After the installation of HEPA filters, outdoor UFP and black carbon pollution events were not reflected in indoor air quality values. Before the deployment of the HEPA filter, half the outdoor particles were measured indoors. After its deployment, only a tenth of the outdoor particles were measured. The study found “83% removal for UFP, 67% removal for aircraft particles and 73% removal for heavy duty traffic particles. This represents a removal efficiency of 83% for removal of particles of outdoor origin.”
• Choi et al. focused on air quality impacts of emissions from motor vehicles and aircraft and employed a mobile monitoring platform to measure real-time air pollutant concentration in West Los Angeles, where Santa Monica Airport is located (2013). The results show that there is a striking feature in intra-community variations in pollutant levels in the neighborhood immediately downwind of the Santa Monica Airport, particularly for UFP concentrations. The highest UFP peak concentrations were associated with mid-size jet takeoffs, followed by small jets and smaller reciprocal-engine aircraft. Similar but less intense trends were observed for CO concentrations. Other pollutants such as NO, PM_2.5 do not have such significant variations in the region.

These studies measured the impact of aircraft emissions on air quality downwind of airports. These studies confirm that airports influence air quality to a greater distance than 1 km. Measurements show the impact spans further than 8 km downwind. Air pollution at nearby residences was impacted by aircraft traffic and wind direction. At a home ~1 km from the Logan International Airport, UFP and NO_x concentrations were greater than or equal to traffic emissions. In addition, residences exposed to aircraft noise also had higher air pollution indoors. Another study found aircraft emissions measured downwind were smaller in size with a narrower size distribution than traffic emissions. This is a concern to airport communities because fine PM is associated with increased pulmonary deposition. Another study deployed HEPA filters into classrooms impacted by aircraft emissions and found an 83% removal for particles of ambient origin. Elevated PM and BC events were no longer reflected indoors after HEPA filters were installed, indicating that some of the indoor air quality impacts from aircraft emissions may be mitigated with appropriate filtration.

5.2.1.2 Transport of Airport Emissions

This section discusses studies that assess airport emission levels, to understand how pollutants disperse and how to distinguish aircraft emissions from other sources.

• Covid-19 restrictions led to the halting of nonessential air travel. A 2020 study by Li & Tartarini quantified the lockdown’s ambient pollutant reduction from the restricted air travel in Singapore. Outdoor pollutant levels were compared prior to the pandemic (2016-2020) and after Singapore enacted strict lockdowns on April 7, 2020. Air quality data were obtained from five monitoring stations: one in the north, south, east (closest to airport), west, and central Singapore. The study found overall pollutant concentrations of NO₂, SO₂, PM_2.5, PM₁₀, and CO decreased by 54, 52, 29, 23, and 6%, respectively. Ozone and CO both increased near the airport, most likely due to heavy industry. The south and west monitors had the greatest reductions in pollutants due to their distance from the airport and heavy industry in the east. The study concludes reduced air travel and industry practices contributed to the reduction in criteria pollutant levels in Singapore.
• Austin et al. assessed emissions at Seattle-Tacoma International Airport via mobile monitoring of CO₂, BC, PM size distribution, and particle counts (2021). Spatial distribution maps of the Seattle-Tacoma Airport, major roadways, and surrounding area were created for UFPs. In line with other literature, the study found that UFP concentrations at the airport were highest under landing paths (north of the airport). Aircraft plumes descended to ground level within a few minutes near the airport. Between 15-20 minutes later, plumes were measured 15 km downwind from the airport. Total UFP concentrations (between 10-1000 nm) were higher on major roadways than downwind of the airport. Roadway traffic emissions were found to be higher in BC and coarse PM than aircraft emissions, aligning with previous studies (Riley et al., 2016, for example) finding larger particle size distributions and coarse PM in traffic emissions.
• A study from Lai et al. looked at particle size distributions for PM and 22 PAHs at the Taipei International Airport, Taiwan (2013). The monitoring site was located 500 m from the runway and 150 m from the taxiway. Measurements of ambient air concentration levels were taken in summer, fall, and winter for comparison. Particulate size distributions were found to vary by season. Winter had the highest total PM and PM₁₀ concentrations, but the UFP concentrations were highest in autumn. The ratio of UFPs to total PM was 0.18 and 0.15 in summer and winter, respectively. For PAHs, the measured concentrations of total particle-bound PAHs equaled 189 and 152 ng/m³ for winter and summer, respectively. Winter typically has the poorest air quality due to inversion layers and reduced rainfall. The three dominant PAHs measured were Naphthalene (2 rings), Phenanthrene (3 rings), and Fluoranthene (4 rings) at 40, 18, and 8.3% of total PAHs, respectively.
• On a national level, the modeling study conducted under PARTNER Project 15 (Ratliff, 2009; Sequeira 2008) found aircraft emissions contributing to the following criteria pollutant concentrations:
  • Annual PM_2.5: 0.01 µg/m³ (0.08%) and
  • 8-hour ozone: 0.10 ppb (0.12%).
• The measurements and modeling conducted under the LAX Source Apportionment Study provided detailed emission contribution information (Tetra Tech 2013). Researchers found CO, NO₂, SO₂, and Pb ambient concentrations within the communities next to LAX were

below threshold levels for state and national standards. PM_2.5 concentrations were near air quality standard levels and had compositions of:

• 50–75% ammonium nitrate, ammonium sulfate, and unapportioned organic matter;
• 20–30% sea salt aerosol, soil-derived fugitive dust, and wood smoke;
• 1–2% jet exhaust; and
• 8–17% diesel plus gasoline vehicle exhaust.
In addition, airport PM_2.5concentration contributions were estimated to be 5–20%. CMAQ modeling showed most of the nitrates, sulfates, and most of the residual organic matter were formed outside of the study area. In winter, the airport accounted for 15–22% of CO and NO_xconcentrations. In summer, the airport accounted for 40–50% of CO and 50–74% of NO_xconcentrations at some measurement sites. Airport SO₂contributions ranged from 10–80% depending on season. HAP concentrations were consistently lower than the levels found elsewhere in the basin area. The generally low concentration levels can be attributed to the coastal location of LAX. Ultrafine composition was found to be largely composed of sulfuric acid aerosols from jet exhaust and their number concentrations east of LAX were found to be higher than typical values in the region.
• Using measured data near London-Heathrow International Airport (LHR), Carslaw et al. (2006) found that aircraft NO_x concentrations could be detected at least 2.6 km from the airport. At the airport boundary, approximately 27 percent of the annual mean NO_x and NO₂ concentrations were found to be due to aircraft. At distances of 2 to 3 km downwind of the airport, an upper limit of 15 percent contribution from the airport was estimated.
• From measurements downwind of Santa Monica Municipal Airport, Hu et al. (2009) found elevated concentrations of UFPs beyond 660 m downwind of the airport. At distances of 100 and 660 m downwind, respectively, UFP concentrations were found to be 10 and 2.5 times greater than background levels.
• Ellerman et al. (2010) used measurement data from Copenhagen Airport to show that the number of UFPs (43,000 particles/cm³) in an apron area was approximately 4.4 times greater than the levels found at a background site (near a major roadway). In contrast, a site located on the east side of the airport (closer to the airport boundary) experienced 12,000 particles/cm³ or 22 percent higher than the same background concentration. The study also found that 90 percent of the particles were in the lower end of the ultrafine size range of 6–40 nm.
• Using measured data near runways at LAX, Hsu et al. (2013) observed median UFP concentrations of 150,000 particles/cm³. In some cases, concentrations exceeded 1,000,000 particles/cm³, which is far in excess of levels seen near roadway sources. However, the concentrations were observed to drop rapidly with distance—by an order of magnitude before reaching the airport boundaries.
• Based on data collected at the LAX blast fence (downwind sites up to 600 m from a runway and upwind of a major runway), Zhu et al. (2011) found high spikes in UFP concentrations. Time-averaged concentrations of PM_2.5, two carbonyl compounds, formaldehyde, and acrolein, were found to be elevated compared to background levels. As UFP and BC levels have previously shown to return to background levels at 300 m downwind for roadway sources, the persistence of airport UFP concentrations up to 600 m seem to indicate that airport emissions may have a broader spatial impact than roadway sources.

• Using data from a monitoring study in the vicinity of LAX, Westerdahl et al. (2008) found the following:
  • Upwind site:
    • UFPs ranged from 58 to 3,800 particles/cm³ at below 90 nm size,
    • NO_x ranged from 4–22 ppb,
    • BC ranged from 0.2–0.6 µg/m³, and
    • PM-PAH ranged from 18–36 ng/m³.
  • Downwind site:
    • UFPs—50,000 particles/cm³, 500 m downwind at 10–15 nm size and
    • Black carbon, PM-PAH, and NO_x levels were “elevated to a lesser extent.”
• A monitoring study near PVD (Rhode Island 2007) found none of the HAP species measured exceeded the acute health and non-cancer benchmarks. However, concentrations of benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acetone, chloroform, carbon tetrachloride, and perchloroethylene exceeded the cancer benchmark levels. Formaldehyde concentrations at all sites were greater than 10 times the cancer risk benchmark. Acetaldehyde and acetone were 2.5–3 times higher than the cancer risk benchmark. BC concentrations in communities were higher in areas near roadways. Although a non-reference method (with a bias towards higher readings) was used to measure PM_2.5, the levels were still below the NAAQS in the communities near the airport. Airport contributions could be identified based on the fidelity of the monitors.
• Based on data collected during a monitoring study around Van Nuys Airport (VNY) and Santa Monica Municipal Airport (SMO) (SCAQMD 2010), it was found that the daily average total suspended PM (TSP) lead concentrations at airport sites were 2–9 times higher than corresponding South Coast Basin levels and mostly below the NAAQS. But 24-hour concentrations at SMO near the tarmac were found to be above the NAAQS on more than one occasion. The highest VOC concentrations at the airport sites were comparable to levels found at urban monitoring sites. PM_2.5 concentration levels, as well as those of OC and EC, were found to be similar to or below the corresponding South Coast Basin averages. UFP numbers measured near a runway were found to be up to 600 times greater than that of background air. Additionally, diurnal profiles suggest that CO concentrations may be mostly due to motor vehicles from surrounding roadways rather than the airport.

Aircraft emission monitoring and modeling studies demonstrate ambient air pollution is impacted by airports, with elevated pollution levels measured at sites at varying distances from an airport. Multiple studies found that UFP concentrations were significantly higher near airports versus major roadways. Studies agreed that criteria pollutants are elevated downwind, far past 1 km. One study measured aircraft contributions to elevated UFP concentrations 15 km from an airport. In another study, 2-3 km from an airport 15% of NO_x emissions were associated with airport contributions. Another study found that there was a slight seasonal variation in emission levels from aircraft. Higher fine, coarse, and total PM, and PAH emission levels were observed in winter while summer was associated with a higher fraction of UFPs. However, UFPs were found to be dominant pollutants near airports year-round.

5.2.1.3 Monitored Indoor Air Quality at Airports

While most research studies on airport quality and health impacts evaluate ambient air quality, some research has recently been conducted on the indoor air quality at airports. The following studies monitored air quality inside airports.

• An indoor air quality study was conducted by Kim et al. on Soekarno-Hatta International Airport in Indonesia, the largest airport in Southeast Asia (2020). In 2019, air quality in Terminal 3 was measured as PM₁, PM_2.5, and PM_2.5-10. Outdoor measurements were also taken to calculate an indoor outdoor (I/O) ratio. The study found outdoor changes in PM₁ lagged by 15 minutes indoors. Lag time for PM_2.5 was 30 minutes indoors. PM_2.5 and PM_2.5-10 were significantly lower indoors versus outdoors but PM₁ was comparable. Indoor air quality was dependent on passenger movement and the previous hour air quality. Fine PM was dependent on aircraft traffic. The I/O ratios were 0.42 for PM₁ and 0.33 for PM_2.5. These ratios indicate that UFPs infiltrate indoors at higher concentrations and faster than PM_2.5 at the Soekarno-Hatta International Airport.
• Targino et al. studied BC concentrations in 12 airports and 41 commercial flights (2017). BC is a climate-warming aerosol with known health implications, such as heart attacks and strokes. BC concentrations are elevated at airports because they originate from combustion. BC concentrations were highest during boarding and disembarking the plane, averaging 3.78 µg/m³. This is from diesel machinery and jet fuel runway emissions infiltrating into the loading zone. There were also high BC concentrations at the airport concourse at a mean concentration of 3.16 µg/m³. When the airplane was parked and the doors were open, BC concentrations were high at mean of 2.78 µg/m³, but when the doors were closed on the ground the concentrations dropped to a mean of 0.81 µg/m³. The lowest concentrations were seen during the flight at a mean of 0.20 µg/m³.
• A study by Hong & Lin measured the CO₂ concentrations in an airport terminal in China (2019). High CO₂ concentrations indoors at the airport are indicative of infiltration of outdoor pollutants to indoor. CO₂ monitors were placed in 50 public areas including departure, arrival, luggage, reception, and check-in. In general, indoor CO₂ levels were low in the airport, even on holidays. Concentrations rarely exceeded 1,000 ppm. Departure areas had the highest CO₂ levels due to the airplane traffic and large passenger volume. On a national holiday the remote departure lounge had the highest CO₂ levels, peaking at 1,640 ppm and averaging 646 ppm. The lowest CO₂ level was observed in the security area, peaking at 754 ppm and averaging 489 ppm.

The studies agree that some ambient pollutant levels are elevated in airports. Airport indoor air quality is most impacted by passenger movement and aircraft traffic outdoors. Pollutants do infiltrate indoors at airports, particularly fine PM. Indoor PM₁ concentrations were comparable to ambient levels, and they infiltrate indoors twice as fast as PM_2.5 (15 min versus 30 min lag time). Passengers are exposed to the highest levels of pollutants in areas open to ambient air, such as arrivals, departures, and aircraft loading and disembarking. Studies show airport air filtration and circulation is successful at reducing indoor air pollutant concentrations.

5.2.2 Health Impact Assessments

5.2.2.1 Criteria Pollutants and HAPs

• Penn et al. put efforts into developing airport-specific health damage functions (deaths per 1,000 t of precursor emissions) and physically interpretable regression models to explain variability in these functions (2017). Modeling results showed that deaths per 1,000 t of primary PM_2.5 emissions ranged from 3 to 160 across airports, with variability explained by population patterns within 500 km of the airport. Deaths per 1,000 t of secondary PM_2.5 precursor emissions varied across airports from 0.1 to 2.7 for NO_x, 0.06 to 2.9 for SO₂, and 0.06 to 11 for VOCs, with variability explained by population patterns and ambient concentrations influencing particle formation. Deaths per 1,000 t of ozone precursors ranged from −0.004 to 1.0 for NO_x and 0.03 to 1.5 for VOCs, with strong seasonality and influence of ambient concentrations.
• Brunelle-Yeung et al. in 2014 adapted existing linearized sources and CMAQ using a 2005 aircraft emissions inventory, census data, and PM_2.5 concentration–response functions to estimate health impacts from aviation emissions. Results showed that, within in the contiguous United States, 210 deaths per year are attributable to aircraft PM emissions (90% confidence interval: 130–340), with total monetized value across mortality and morbidity of $1.4 billion per year in year 2000 U.S. dollars (90% confidence interval: $550 million–$2.8 billion).
• On a national level, a system-level health risk assessment study (Levy 2012) using CMAQ and appropriate concentration–response functions (CRF) to model baseline and future scenarios determined that national population health impacts would increase by a factor of 6.1 from 2005 to 2025. This was based on a notional “what if” aviation growth scenario and corresponding emissions assumptions. The factor of 6.1 increase was decomposed into the following contributing factors: Emissions: 2.1; Population factors (growth and aging): 1.3; and Changing non-aviation concentrations, enhancing PM_2.5 formation: 2.3.
• Schlenker & Walker analyzed air traffic data and concluded that excess airplane idling, measured as residual daily taxi time, is due to network delays originating in the eastern U.S., and this idiosyncratic variation in daily airplane taxi time significantly impacts the health of local residents, largely driven by increased levels of CO exposure (2016). The author used this variation in daily airport congestion to estimate the population dose response of health outcomes to daily CO exposure, examining hospitalization rates for asthma, respiratory, and heart-related emergency room admissions. The results showed that a one standard deviation increase in daily pollution levels leads to an additional $540 thousand in hospitalization costs for respiratory and heart-related admissions for the 6 million individuals living within 10 km (6.2 miles) of the airports in California. The author also noted the health effects occur at levels of CO exposure below current EPA standards and lowering the CO NAAQS could lead to sizable morbidity benefits.
• Based on a health impact study of UK airport expansions, especially LHR (i.e., third runway), it was estimated (Barrett et al. 2012 and Yim 2013) approximately 110 people in the United Kingdom die early each year due to airport emissions. Of these deaths,

approximately 50 are due to emissions from LHR. By 2030, without airport capacity expansion, the number of early deaths per year caused by UK airport emissions is projected to increase to 250.

• In contrast, an FAA Office of Environment and Energy report in 2015 listed 8 metropolitan areas and stated that aviation emissions’ contribution to those areas’ total emission inventories is generally small. The report also quoted research and stated that of the premature mortalities related to PM emissions from all combustion sources, only less than 1% are associated with aviation emissions.

5.2.2.2 UFPs

As discussed in previous chapters, UFPs have a greater pulmonary deposition than fine and coarse PM. In terms of aircraft emissions, jet fuel emissions are characteristically high in UFPs between 10-20 nm in size, unlike diesel and traffic emissions. The following studies demonstrate the health impacts of UFPs on communities and EJ considerations.

• Lammers et al. (2020) investigated effects of short-term exposures to UFPs near an airport in healthy subjects. Twenty-one healthy adults were repeatedly (2–5 visits) exposed to ambient air near Schiphol Airport, while performing 5 hours of intermittent moderate exercise. Pre- to post-exposure changes in cardiopulmonary outcomes (spirometry, forced exhaled nitric oxide, electrocardiography, and blood pressure) were assessed and related to total- and size-specific PNCs, using linear mixed effect models. The results show that short-term exposures to aviation-related UFP near a major airport were associated with decreased lung function (mainly forced vital capacity [FVC]) in healthy volunteers. The effects were relatively small, however, they appeared after single exposures of 5 hours in young healthy adults.
• Habre et al. demonstrates increased acute systemic inflammation following exposure to airport-related UFPs, which were distinct from traffic-related exposures (2018).
• Selley et al. conducted a field exposure experiment near Amsterdam Airport Schiphol that shows aviation UFPs were associated with significant reductions in urinary taurine, dimethylamine concentrations and pyroglutamate concentrations (2021).
• Corlin et al. studied annual average UFP exposure and systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and hypertension among 409 adults (2018). Air pollution measurements included PNC mobile monitoring. Overall, the study found exposure to UFPs was “positively, though not significantly, associated with SBP, PP, and hypertension prevalence.” No association was found between DBP and UFP

exposure for any group. Associations of SBP, PP, and hypertension were more positively associated with:

• Non-Hispanic Whites versus other races/ethnicities.
• Participants with diabetes versus without diabetes (for SBP and PP).
• Participants with hypertension versus without hypertension.
• Tucker et al. conducted UFP PNC measurements in Puerto Rican residents living in Boston, MA (2010). The two-year study found Puerto Rican populations had elevated high sensitivity C-reactive protein (hsCRP) associated with traffic emissions. This study had 1,499 participants where average UFP particle inhalation rate (PIR) was compared to blood pressure and hsCRP readings. Readings were performed three times per participant between 2004-2015. The study found the PNCs were not significantly associated with elevated SBP. Females and never smokers had a positive association between PNC and SBP. Males had an inverse association. Rather than PNC, researchers also calculated the PIR that was positively associated with SBP. The generally healthy participants had stronger associations. A similar finding to other studies, PNC and DBP were not associated.

In these studies, participants were exposed to elevated UFP counts and measured for health markers, such as blood pressure, hypertension, and urinary metabolism. UFP exposure was associated with elevated systolic blood pressure, PP, and hypertension; although, the correlation was not significant. Correlations were stronger for participants with pre-existing health conditions. One study did find a significant correlation to SBP when compared to the PIR (versus UFP counts). Studies did not find an association between diastolic blood pressure and UFP exposure. The findings of these studies need to be researched further and should not be considered to be demonstrative or predictive of health impacts. These studies were based on small sample sizes with limited human exposures.

More research is needed on the health impacts of UFPs from aircraft on nearby communities. This includes a broad look at health care in general in EJ communities, understanding the key pollutants and exposures within these communities, and working with these communities to address existing airport-related health problems and developing strategies to eliminate or mitigate these impacts where possible.

5.2.2.3 Lead

Lead emissions from AvGas combustion have become a rising concern in airport communities. There has been ongoing monitoring of lead concentrations in different areas within the airport and in the vicinity of airport fence lines. Studies also try to correlate residential proximity to the airport with blood lead level surveillance data to identify aviation lead emission geodemographic impact. Findings from studies on the health impacts of lead from aircraft are summarized below.

• In a study released in 2020, EPA estimated ranges of lead concentrations that may occur at and near airports where leaded aviation gas is used (U.S. EPA. 2020). The study extrapolated modeling results to estimate air lead concentrations at the “maximum impact area” for over 13,000 U.S. airports. The “maximum impact area” is the assigned area at the end of the runway where pilots are required to conduct safety checks with engines running just prior to takeoff. This area is expected to have the highest concentration of lead in air, and in fact air monitoring has reported concentrations of lead above the lead NAAQS near this location at some airports. The model-extrapolated lead estimates in this study indicate

that some additional U.S. airports may have air lead concentrations above the NAAQS at this area of maximum impact. The report also shows that estimated lead concentrations decrease to below the standard within 50 meters from the area of highest concentration. Estimated lead concentrations from this study should not be used to directly evaluate attainment of the lead NAAQS.

• One study in North Carolina, using GIS and blood lead surveillance data, shows that children living within 1,000 m of an airport where planes use leaded AvGas have higher blood lead levels than other children (Miranda et al. 2011). The effect of AvGas on blood lead levels exhibited a monotonically decreasing dose-response pattern, with the largest impact on children living within 500 m of an airport.
• To study the potential health impact on adults of lead from piston-engine air traffic, Klemick et al. (2022) analyzed air traffic data and cardiovascular mortality among adults aged 65 and older near 40 North Carolina airports from 2000 to 2017. Results show no consistently significant association between cardiovascular mortality rates and piston-engine air traffic near multi-runway airports. The authors of this study concluded “Our findings are mixed but suggestive of adverse effects. We found higher rates of cardiovascular mortality within a few kilometers downwind of single- and multi-runway airports, though these results are not always statistically significant. We also found significantly higher cardiovascular mortality rates within a few kilometers and downwind of single-runway airports in years with more piston-engine air traffic.”
• One socioeconomic study (Zahran et.al. 2017) finds that child blood lead levels (1) increase dose-responsively in proximity to airports, (2) decline measurably among children sampled in the months after 9/11, (3) increase dose-responsively in the flow of piston-engine aircraft traffic, (4) increase in the percentage of prevailing wind days drifting in the direction of a child’s residential location, and (5) behave intuitively and significantly when considering two-way and three-way interactions of the main treatment variables. The study quantifies that damages from AvGas are at least $10 per gallon.
• One research study in Santa Clara County, California shows residential distance plays an important role in child blood lead levels (BLLs), compatible with the exposure risk to AvGas (Mountain Data Group 2021). Children within 0.5 miles of Reid-Hillview Airport have BLLs that are about 0.2 µg/dL higher than statistically similar children more distant. Moreover, children residing downwind of the airport have an additional 0.4 µg/dL higher BLL than those proximate to the airport but not in the downwind direction. Another supporting evidence shows that child BLLs increase significantly with exposure to piston-engine aircraft operations. From the minimum to the maximum of child piston-engine aircraft traffic exposure, child BLLs increase by 0.163 to 0.387 µg/dL, depending on the presence of control variables. Results also indicate children residing within 0.5 miles of the airport are more vulnerable to increased piston-engine traffic than those at longer distances. This report concludes that proximity to the airport, and specifically, the exposure to piston-engine air traffic and AvGas combustion, will increase child BLL. It also quotes the National Academies of Sciences, Engineering, and Medicine: “Because lead does not appear to exhibit a minimum concentration in blood below which there are no health effects, there is a compelling reason to reduce or eliminate aviation lead emissions.”

5.2.3 Children and Infant Exposure Assessments

Children are disproportionately affected by pollutant exposure. Children are at greater risk due to underdeveloped defense systems and increased particle inhalation rates. For example, lead exposure contributes to neurological effects and ozone increases asthma hospitalizations in children. The findings of some studies that looked at the health impacts to children and infants of exposure to airport pollutants are summarized below.

• Wing et al. conducted a population-based study using birth records to ascertain birth outcomes and a novel, validated geospatial UFP dispersion model approach to estimate in utero exposures (2020). Results suggest in utero exposure to aircraft-origin UFPs was positively associated with PTB (preterm birth), independent of traffic-related air pollution exposures.
• Clifford et al. measured UFP, NO_x, and respiratory health to better understand impacts on school children (2018). This study’s participants live in Brisbane, Australia and are aged 8-11 years. PNCs were measured at two indoor and three outdoor monitors. The study found exposure to UFP PNCs was associated with an increase in inflammation. Elevated PNCs were associated with decreased FeNO, a health marker for eosinophilic airway inflammation. The study also found an association between UFPs and systematic inflammation, measured by C-reactive protein. C-reactive protein levels are biomarkers for heart disease risk. This study therefore shows UFP health consequences go beyond the respiratory system.

The studies on infant and school children pollutant exposure effects were predominantly focused on the health impacts of UFPs, PM, ozone, and lead (lead studies are discussed in section 5.2.2.3). One study suggests that pregnant women exposed to UFPs may have a higher rate of preterm births. These studies suggest children exposed to criteria pollutants and UFPs are at increased risk for respiratory, cardiovascular, and neurological diseases.

5.2.4 Airport Employee Exposure Assessments

A passenger’s exposure to airport pollutants is generally short. In contrast, certain airport workers may spend extensive time in polluted areas. This is especially true for fuel operation workers and baggage handlers working outside near idle aircraft and the runway. The following studies measured pollutant and exposures health impacts of airport workers to assess their risk factors.

• A Masiol & Harrison study found a strong gradient of exposure to UFP in ambient air across occupational groups of airport employees (2014). Baggage handlers were exposed to 7 times higher average concentrations than employees mainly working indoors. Catering drivers, cleaning staff and airside security were exposed to intermediate concentrations. In another study, airport fuel workers underwent breath samples before and after work. The study found fuel workers were exposed to more than 100 times higher levels of pollutants than the control group. These findings support those of Targino et al., who found passenger BC exposure was highest boarding and disembarking the plane.
• The results of an airport occupational exposure study (Tunnicliffe et al. 1999) conducted at Birmingham International Airport, UK, support an association between high occupational exposures to aviation fuel or jet stream exhaust and excess upper and lower

respiratory tract symptoms for airport male workers. However, it is acknowledged that there could have been some bias effects such as residual confounding due to smoking.

• A register-based follow-up study based on a cohort comprising an exposed group of 6,515 men employed in unskilled work at Copenhagen Airport and a reference group of 61,617 men in unskilled work in other firms in greater Copenhagen during 1990–2012 did not see an association between outdoor occupational exposure to UFP and IHD (ischemic heart disease) and cerebrovascular disease (Møller et al., 2020).

Some studies found airport workers are at higher risk for pollutant exposure. Baggage handlers are exposed to 7 times higher pollutant concentrations than employees working indoors. Jet fuel workers are exposed to more than 100 times higher pollutants than a control group. Another study found male airport workers exposed to jet fuel experienced upper and lower respiratory tract symptoms. Indoor workers, such as catering drivers, cleaning staff, and airside security, were exposed to intermediate concentrations. Jet fuel workers can minimize their pollutant exposure with proper technique, minimizing splashing, and dermal protection; however, jet fuel workers are still exposed to runway and idle aircraft emissions. One study found no correlation between UFP exposure and ischemic heart disease and cerebrovascular disease in airport workers. Overall, the airport worker exposure studies demonstrate outdoor airport employees are exposed to far greater pollutant levels than indoor airport workers, passengers, and airport communities.

There are several operational measures to reduce aviation lead emissions. Aircraft fueling operation measures mainly include elimination of accidental overfilling, splashing, and spills onto the aircraft, ground, and body and clothes of the person. Pre-flight fuel sampling leads to discarding of the fuel to the ground, and this is a practice that can be regulated. More frequent and enhanced aircraft maintenance will reduce the risk of occupational exposure of the airport personnel. Run-up leads to peak emission concentrations, and potential relocation of these run-up areas will reduce the overall peak concentrations.

5.2.5 EJ Assessments of Airport Communities

EJ concerns for airport communities have become increasingly apparent in recent years. As modeling and monitoring studies demonstrate, airport emissions travel kilometers downwind and place a health burden on nearby residents.

Airports and EJ are discussed concurrently for two main reasons:

1. Locally unwanted land uses. Residential areas in the proximity of nuisances, such as airports, tend to be disproportionately populated by historically marginalized people (Been, 1994). This has historically been due to discriminatory housing practices, less access to resources to leave the nuisance, and lower residential costs.
2. Power to resist. This questions if airport developments and expansions are more likely to occur in communities without the power to resist them. In other words, the largest airports generally have the highest levels of nuisances and percentages of historically marginalized people. The general population has become more educated on air, noise, and light pollution, which has encouraged wealthy areas to minimize these exposures. In contrast, while residents of low-income communities may also be aware of these concerns, they may not have the available resources (including power, money, and time) to find ways to resist or minimize exposures, or to move away. After an airport is built, the surrounding area becomes “undesirable” to long-term homeowners. The

real estate is often inexpensive and may be purchased by investors for short-term rental properties. Renters are more likely to be low-income and disproportionately take on EJ issues associated with airport communities (Grineski et al., 2007; Woodburn, 2016).

When EPA strengthened the primary annual PM_2.5 NAAQS in February 2024, it also modified the monitoring network design criteria, adding a factor that accounts for the proximity of populations at increased risk of health effects from sources of PM_2.5 air pollution. Areas that have a requirement for additional monitors as part of their existing State/Local Air Monitoring Stations (SLAMS) network are now required to site a monitor in an at-risk community with poor air quality. Airports are one of the identified types of sources where these monitors might be needed. Having these additional monitors in EJ communities will enable EPA to ensure that future NAAQS reviews include local data from these overburdened communities. Such monitoring data should also be valuable in future modeling studies of air quality near airports and research on the heath impacts of airports on EJ communities.

Census data shows historically marginalized people are more likely to live near airports, likely due, at least in part, to the factors discussed above. The following studies examine the disproportionate burden of negative environmental air quality impacts on airport communities.

• A 2013 case study by Rissman et al. found that in airport communities, the higher the UFP concentration, the higher the minority population at ATL. Overall, those strongly impacted by aircraft emissions had less education and less income.
• Johnson et al. grouped nearby communities by distance to the Seattle-Tacoma International Airport (2020). The baseline was the general King County area greater than 10 miles from the airport. The study found poverty rates, immigrant populations, and all-cause mortality increased with proximity to the airport. Three-quarters of the Black population living in King County lived within 10 miles of the airport. The average life expectancy for residents living less than 1 mile from the airport was 5 years less than for the general King County area. Airport communities had 1.1-2.3 times higher deaths from cancer as well as elevated heart disease and diabetes risk. Children were particularly impacted with elevated asthma occurrences, premature birth rates, and lower high school graduation rates. This study suggests that airport emissions may negatively impact the health of nearby residents, who are disproportionately minority or low-income.
• Rissman et al. studied the impacts of aircraft emissions on concentrations of PM_2.5, at ATL using modeled data for June-July 2002 (2013). On average, aircraft contributed 232 ng/m³ (232 × 10−9 g/m³) to surface PM_2.5 concentrations in the airport’s grid cell (4 km × 4 km), accounting for roughly 2–3% of the total surface PM_2.5 concentrations in that grid cell. Distributional analyses show that within 675 of 710 census tracts of the 29-county Atlanta metropolitan region, the average aircraft contribution to PM_2.5 was low (<20 ng/m³). The study also noted in tracts where aircraft contribution to PM_2.5 exceeded 20 ng/m³ (36 tracts), populations overwhelmingly had lower median incomes, home values, and educational attainment, as well as higher percentages of nonwhite minority residents. Analysis using a concentration–response function indicates that the airport’s influence on PM_2.5 concentrations may be responsible for ≈1.4 premature adult (age 25+) deaths per year.
• In the Technical Support Document (TSD) that accompanied the aircraft lead Endangerment Finding (see Section 4.1), EPA provided lead emission data from piston-engine aircraft, a description of the concentrations of lead in air resulting from emissions

of lead from piston-engine aircraft, a summary of and the fate and transport of lead from piston-engine aircraft, and a description of how many people live in close proximity to airports where they may be exposed to airborne lead from aircraft engine emissions of lead (U.S. EPA 2023).

As described in the TSD, EPA quantified the number of people living, and children attending school, within 500 meters of the approximately 20,000 airports in the U.S. The analysis found that approximately 5.2 million people live within 500 meters of an airport runway, 363,000 of whom are children aged five and under. The EPA also estimated that 573 schools attended by 163,000 children in kindergarten through twelfth grade are within 500 meters of an airport runway.

The results of the proximity analysis also found that three states (Nevada, South Carolina, and South Dakota) have higher percentages of children five and under living in the near-airport community, as compared with the overall state population. Nine states (California, Kansas, Kentucky, Louisiana, Mississippi, Nevada, South Carolina, West Virginia, and Wisconsin) have Black populations representing greater fractions of the population in the near-airport community compared with the state. There are three states (Indiana, Maine, and New Hampshire) where Asians and five states (Alaska, Arizona, Delaware, New Mexico, and South Dakota) where Native Americans and Alaska Natives experience similar disparities. In a separate analysis, data indicate that there is a greater prevalence of children under five years and people of color and of lower income within 500 meters to one kilometer of some airports compared to more distant locations.

To study the geodemographic impact of lead from piston-engine aircraft, EPA modeled the total population as well as the population in educational facilities within 500m of the airport runway. The report found that approximately 5.2 million people live, and 163,000 children (grades K-12) attend schools, within 500 meters of an airport runway. This report does not assess the risk or characterize air lead concentrations for this population.

The findings of the EJ studies in neighborhoods near airports show that elevated UFP and CO concentrations were correlated to greater minority populations, less income, and less education. Health census data shows these populations are impacted by airport emissions. Studies could not determine if historically marginalized communities moved to airports after being built due to a lower cost of living or if airports were purposely constructed in historically marginalized communities with little power to resist.

5.2.6 Summaries and Conclusions

The example findings discussed in the previous sections illustrate the types of quantitative and investigative studies that have been conducted on airport contributions to local air quality and health impacts. They also illustrate that airport concentration contributions and health impact statistics are closely related. Although the types and scope of these studies vary, they help to form a picture of the current understanding of airport health impacts.

In summary, it should be noted that all pollutants emitted from airports have some level of toxicity with the potential to cause health effects. As stated earlier, each airport is different and can have significantly different emissions, weather patterns, geography, etc., from each other, resulting in different air quality contributions. With that in mind, the existing body of research appears to suggest the following for each pollutant (or category of pollutants):

• Most criteria gases (CO, NO₂, and SO₂)—In most situations, airport contributions of these pollutants appear to be such that resulting ambient community or urban concentrations are generally below the NAAQS. Depending on the pollutant and distances to the affected communities, airport contributions of these pollutants may be relatively small. However, as studies have pointed out, the contributions can still be apportioned at relatively far distances (a few miles). The evidence supporting quantitative health risk assessment is more limited for CO, NO₂, and SO₂, relative to ozone and fine PM, mainly because levels of these pollutants tend to be below the NAAQS.
• In general, most studies suggest that ozone levels in the vicinity of airports tend to be lower than background levels and health assessments indicate the risks associated with airport indirect ozone contributions to local air quality are relatively small. This is due in part to chemical reactions in the air that may slightly reduce ozone levels close to the source of NO_x emissions. However, airport emissions may contribute to higher regional ozone levels downwind.

• Lead is a source of concern due to its toxicity and use at GA airports. Modeling and measurement efforts have shown that lead emissions from GA airports can persist up to 1,000 m downwind and may be above the background and the NAAQS concentrations. Evidence has shown aviation lead emissions can cause elevated blood lead levels in human bodies. There is no evidence of a threshold below which there are no harmful effects on cognition in children from lead exposure. To address this concern, EPA (2023) issued a finding that engine emissions of lead from certain aircraft cause or contribute to the lead air pollution that may reasonably be anticipated to endanger public health and welfare. As a result of this endangerment finding, EPA is now directed to propose and promulgate regulatory standards for lead emissions from certain aircraft engines. Under their own authority, FAA is now obligated to develop standards that address the composition or chemical or physical properties of an aircraft fuel or fuel additive to control or eliminate aircraft lead emissions. Separate from the endangerment finding, since 2022, the FAA is also actively partnering with the GA industry under the Eliminate Aviation Gasoline Lead Emissions (EAGLE) initiative to safely eliminate lead emissions from aviation gasoline by the end of 2030.

• PM_2.5 or fine particles are a serious concern for health impacts as they dominate air quality health risks (e.g., by orders of magnitude over HAPs). The levels found in airport measurement studies vary, ranging from relatively low levels to those that are close to the NAAQS, and in some cases exceeding the standards.

In addition to the variability of PM_2.5 contributions, the various components and types of PM including BC, nitrates, sulfates, and volatiles need to be recognized as well. Modeling studies suggest that secondary PM may form much farther downstream (many miles). As such, the total health impacts from airport-emitted PM and PM precursors requires regional-scale atmospheric modeling. EPA has determined that “…the evidence does not indicate that any one source or component is more strongly related with health effects than PM_2.5 mass” (U.S. EPA, 2022), PM₁₀ is also a health concern, but to a lesser degree than PM_2.5 because coarse particles (PM_10-2.5) are filtered to a greater extent by the upper respiratory tract in humans.

• UFPs are a suspected major health concern but there is limited data available on both particle concentrations and resulting health effects. However, existing studies indicate that UFP concentrations are elevated at an airport (i.e., near a runway) with particle counts that may be orders of magnitude higher than background with some persistence many meters downstream (e.g., 600 m).
• Not all studies have shown consistent health impact results for those affected by these elevated UFP concentrations. For example, some studies have found that exposure to airport UFP emissions may lead to acute systematic inflammation, decreased lung functions, and preterm births. Other studies indicate that airport UFPs have small to no association to ischemic heart disease, cerebrovascular disease, or urinary metabolism.

• While HAPs or air toxics may pose less of a health risk than PM_2.5, they still pose a concern due to the potential for cancer and premature death endpoints. In addition, HAPs like acrolein and the aldehydes also have the potential to cause respiratory effects. Measurement studies indicate that concentration levels can vary significantly from one airport to another. Although some studies suggest monitored concentrations may be comparable to background levels (depending on where the measurements were conducted), there is also enough evidence to suggest that airport contributions are not negligible.