Intersections as Hot Spots: Assessing the Contribution of Localized Nontailpipe Emissions and Noise on the...

Traffic emissions comprise a complex mixture of pollutants, including tailpipe (exhaust), nontailpipe (e.g., brake and tire wear, resuspended road dust), and noise pollution. While regulations have drastically reduced tailpipe emissions, the growing vehicle fleet and miles traveled have contributed to rising levels of nontailpipe and noise emissions. These under-regulated exposures, often rich in toxic transition metals, may pose significant health risks, particularly when noise acts as a co-exposure that may influence traffic-related effects.

We examined lung function effects associated with nontailpipe exposures in the Children's Health Study (CHS), a cohort of over 1,200 children from eight Southern California communities. Participants underwent lung function testing for forced expiratory volume in 1 second (FEV₁) and forced vital capacity (FVC) at three time points between 2008 and 2012.

Participant-specific estimates of particulate matter (PM) mass and metal exposures were developed by applying spatiotemporal regression models to measurements of quasi-ultrafine (PM_0.2, aerodynamic diameter ≤0.2 µm), fine (PM_0.2-2.5), and coarse (PM_2.5-10) particles collected at 220 locations across two seasons. We used supervised variable selection of over 150 variables, including meteorology, roadway and traffic characteristics, land use, and dispersion-modeled estimates of tailpipe emissions. Particular attention was paid to PM components with high oxidative potential or those serving as markers for nontailpipe sources.

Traffic noise exposures were estimated at a 20-m resolution along roadways using two approaches: (1) mobile measurements of A-weighted equivalent noise levels (LAeq in decibels [dB]) collected in Long Beach and modeled using machine learning; and (2) CadnaA, an acoustic model applied to generate LAeq across all communities.

We used mixed effects models to evaluate associations between exposures and lung function, adjusting for covariates and accounting for repeated measures.

Spatiotemporal PM models showed strong performance (leave-one-community-out cross-validation coefficient of determination [R²] 0.59-0.82; 10-fold cross-validation R² 0.76-0.92). Incorporating intersection-based hotspots and meteorology substantially improved accuracy.

Noise models using extreme gradient boosting (XGB) achieved high accuracy (leave-one-route-out cross-validation R² = 0.71, root mean square error [RMSE] 4.54 dB; fivefold cross-validation R² = 0.96, RMSE 1.8 dB), with traffic volume, road proximity, and meteorology as top predictors. LAeq estimates from XGB performed well on smaller roads, while CadnaA performed better on highways. Both approaches captured fine-scale spatial variation in traffic-related noise.

Epidemiologically, coarse and fine nontailpipe metals, especially iron (Fe) and copper (Cu) as markers of road dust and brake wear, were consistently and significantly associated with reduced FEV₁ and FVC. These associations persisted after adjusting for PM mass, and were stronger than those of tailpipe markers like fine elemental carbon (EC_2.5, diesel) and quasi-ultrafine organic carbon (OC_0.2, gasoline).

Traffic noise was not independently associated with lung function but strongly confounded the OC_0.2-FEV₁ association (noise-adjusted FEV₁ was 33% larger in magnitude), suggesting co-occurrence or shared spatial patterns between noise and tailpipe emissions. In contrast, green space was directly associated with better lung function and acted as a confounder, attenuating nontailpipe PM component associations while strengthening that of OC_0.2.

This study highlights the lung function impacts of PM components, the role of traffic noise, and the influence of green space. From an exposure modeling standpoint, a major takeaway is the importance of meteorology and intersection features for PM components, and meteorology, traffic volume, and land use for traffic noise.

Nontailpipe metals had the strongest and most consistent associations with reduced lung function, likely via oxidative stress pathways. Noise influenced the associations of tailpipe markers, reflecting a separate stress mechanism. Green space emerged as a protective factor, highlighting its potential to influence adverse associations between traffic-related air pollution and children's respiratory health.

As nontailpipe sources grow in importance with increased electric vehicle adoption, mitigation strategies should target brake, tire, and road wear. Regulatory actions, lighter electric vehicle designs, and regenerative braking could help. Simultaneously, expanding green space offers a feasible, co-beneficial intervention to buffer both air and noise pollution and to promote children's respiratory health.