Statistical approaches for identifying air pollutant mixtures associated with aircraft departures at Los Angeles International Airport

A Systematic Review of The Impact of Commercial Aircraft Activity on Air Quality Near Airports

Commercial airport activity can adversely impact air quality in the vicinity of airports, and millions of people live close to major airports in the United States. Because of these potential impacts, a systematic literature review was conducted to identify peer reviewed literature on air quality near commercial airports and assess the quality of the studies. The systematic review included reference database searches in PubMed, Web of Science, and Google Scholar, inclusive of years 2000 through 2020. We identified 3,301 articles, and based on the inclusion and exclusion criteria developed, seventy studies were identified for extraction and evaluation using a combination of supervised machine learning and manual screening techniques. These studies consistently showed that ultrafine particulate matter (UFP) is elevated in and around airports. Furthermore, many studies show elevated levels of particulate matter under 2.5 microns in diameter (PM2.5), black carbon, criteria pollutants, and polycyclic aromatic hydrocarbons as well. Finally, the systematic review, while not focused on health effects, identified a limited number of on-topic references reporting adverse health effects impacts, including increased rates of premature death, pre-term births, decreased lung function, oxidative DNA damage and childhood leukemia. More research is needed linking particle size distributions to specific airport activities, and proximity to airports, characterizing relationships between different pollutants, evaluating long-term impacts, and improving our understanding of health effects.

Short-term effects of airport-associated ultrafine particle exposure on lung function and inflammation in adults with asthma

BACKGROUND

Exposure to ultrafine particles (UFP, particles with aerodynamic diameter < 100 nm) is associated with reduced lung function and airway inflammation in individuals with asthma. Recently, elevated UFP number concentrations (PN) from aircraft landing and takeoff activity were identified downwind of the Los Angeles International Airport (LAX) but little is known about the health impacts of airport-related UFP exposure.

METHODS

We conducted a randomized crossover study of 22 non-smoking adults with mild to moderate asthma in Nov-Dec 2014 and May-Jul 2015 to investigate short-term effects of exposure to LAX airport-related UFPs. Participants conducted scripted, mild walking activity on two occasions in public parks inside (exposure) and outside (control) of the high UFP zone. Spirometry, multiple flow exhaled nitric oxide, and circulating inflammatory cytokines were measured before and after exposure. Personal UFP PN and lung deposited surface area (LDSA) and stationary UFP PN, black carbon (BC), particle-bound PAHs (PB-PAH), ozone (O3), carbon dioxide (CO2) and particulate matter (PM2.5) mass were measured. Source apportionment analysis was conducted to distinguish aircraft from roadway traffic related UFP sources. Health models investigated within-subject changes in outcomes as a function of pollutants and source factors.

RESULTS

A high two-hour walking period average contrast of ~34,000 particles·cm-3 was achieved with mean (std) PN concentrations of 53,342 (25,529) and 19,557 (11,131) particles·cm-3 and mean (std) particle size of 28.7 (9.5) and 33.2 (11.5) at the exposure and control site, respectively. Principal components analysis differentiated airport UFPs (PN), roadway traffic (BC, PB-PAH), PM mass (PM2.5, PM10), and secondary photochemistry (O3) sources. A standard deviation increase in the 'Airport UFPs' factor was significantly associated with IL-6, a circulating marker of inflammation (single-pollutant model: 0.21, 95% CI = 0.08-0.34; multi-pollutant model: 0.18, 0.04-0.32). The 'Traffic' factor was significantly associated with lower Forced Expiratory Volume in 1 s (FEV1) (single-pollutant model: -1.52, -2.28 to -0.77) and elevated sTNFrII (single-pollutant model: 36.47; 6.03-66.91; multi-pollutant model: 64.38; 6.30-122.46). No consistent associations were observed with exhaled nitric oxide.

CONCLUSIONS

To our knowledge, our study is the first to demonstrate increased acute systemic inflammation following exposure to airport-related UFPs. Health effects associated with roadway traffic exposure were distinct. This study emphasizes the importance of multi-pollutant measurements and modeling techniques to disentangle sources of UFPs contributing to the complex urban air pollution mixture and to evaluate population health risks.

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures

The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

A comparison between monitoring and dispersion modeling approaches to assess the impact of aviation on concentrations of black carbon and nitrogen oxides at Los Angeles International Airport

Aircraft activity and airport operations can increase combustion-related air pollutant concentrations, but it is difficult to distinguish aviation emissions from traffic and other local sources. Emission inventories are uncertain and dispersion models may not capture aircraft plume complexity; ambient monitoring data require detailed statistical analyses to extract aviation signals. The goal of this study is to compare two modeling approaches including monitoring-based regression models and the EDMS/AERMOD dispersion model, informing improvements and allowing quantitation of aviation impacts on air quality through multi-pollutant sensitivity and multi-monitor fate/transport analyses. Aggregate concentration comparisons are similar, though diurnal patterns show potential weaknesses in near-field dispersion, treatment of overnight conditions, and emission inventory accuracy.