Hypothesis: Traffic activities create substantial exposures to environmental stressors for both commuters and residents, thereby contributing to environmental disparities and adverse population health outcomes. Study Aims: Using modeling approaches to characterize the impacts of Traffic Related Environmental Stressors (TRES) on the population health at the community level. Methods: To comprehensively quantify community-level TRES that include airborne nitrogen oxides (NOx), diesel exhaust particles (DEP) and noise, this work employs and evaluates a variety of methodologies, focusing on areas across the state of New Jersey during the past two decades. Methodologies include analysis of monitor network observations, outcomes of the RLINE (the Research LINE-source dispersion) model, CMAQ (the Community Multi scale Air Quality model), TNM (Traffic Noise Model), Machine Learning derived air pollution estimates and existing databases of relevant environmental and sociodemographic metrics.Furthermore, insights from local commuting trajectories and mobile air monitor measurements collected through the Rutgers Commuter Community Cohort (RC3) study are utilized to evaluate the spatial gradients of TRES as well as their impacts on commuter exposures. The efficacy and consistency of diverse approaches for characterizing TRES burdens at the community level is critically evaluated. Additionally, the modeling framework is employed to assess the potential impact of technological innovations in combustion engines on local air quality and the corresponding exposures. The study further investigates associations of TRES exposures and population-level health outcomes, such as chronic obstructive pulmonary disease, cardiovascular disease, and COVID-19, across New Jersey, using municipality level data and our laboratory’s socioexposomic analysis framework. This framework uses a knowledge-guided data-driven approach to investigate complex interactions among spatially heterogeneous environmental, demographic, socioeconomic and behavioral factors. Given the known associations of TRES with demographic and socioeconomic indicators, it is important to evaluate health risks within a multi-attribute socioexposomic framework, where both social and environmental determinants of health are taken into account. Geostatistical models and machine learning models are implemented to explore and characterize association patterns. Applications: This work conducts case studies of TRES exposures primarily at the regional and local levels, focusing on the New Jersey Industrial/Transportation Corridor (NJITC) and on environmental justice neighborhoods in New Jersey. Additionally, a case study assessing the potential impact of technological innovation on TRES exposures is carried out at the national level, across the contiguous United States (CONUS). Significance: This work implements and tests a systematic modeling framework and develops publicly available software tools supporting characterization of impacts of factors associated with TRES on potential health risks. The modeling framework provides a platform for evaluating impacts of new regulations and technologies on population exposures to TRES.
Zhongyuan Mi (Thu,) studied this question.