Traffic-related air pollution has been associated with adverse cardiovascular health effects in near-road residents. Transportation parameters are important surrogate variables to determine spatial variation of air pollution and consequential health outcomes. We used land-use regression models to explore associations between cardiovascular (metabolic syndrome [MetS]) health outcomes collected from a sample of low-income participants (N = 4,959) and transportation parameters within a defined impact zone of a participant’s residence. We hypothesize cardiovascular risk factors are associated with spatially distributed transportation parameters and land-use data. MetS risk factors (waist circumference, blood pressure, triglycerides, HDL-cholesterol, and glucose) were obtained from 4,945 participants between 2014 and 2020 across the city of El Paso, Texas. Traffic-related and land-use variables were acquired from the El Paso MPO and the U.S. Census Bureau within two impact zones of 500 m and 1,000 m radius, centered at each participant resident’s home latitude and longitude coordinates using GIS. The increase in street length within 500 m radius was found to associate with increases in BMI, waist circumference, triglycerides, and glucose (p < 0.05). Glucose showed positive relationships with inverse distance to the nearest international ports of entry (p = 0.02). Also, as the total length of the street increases, the likelihood of a high waist (p < 0.01), high triglycerides (p = 0.03), and low HDL-cholesterol (p < 0.01) also increased. Based on a multivariable regression model, a probability surface map was prepared to show the spatial distribution of likelihood for acquiring MetS in El Paso, TX.

Traffic-related air pollutants are especially prevalent in border cities with multiple ports of entry (POEs). Excessive emissions due to long delays of idling commercial and passenger vehicles at the POEs could exacerbate various types of respiratory health conditions for the POE users and workers. We conducted a 1-month in-traffic air monitoring campaign at a POE in El Paso, Texas using three continuous FEM instruments for three criteria pollutants (PM, O₃, and NO₂). Concurrent monitoring of PM in the nearby community was performed using a number of low-cost PM sensors. The 1-month average PM_2.5 concentration at the POE was found below 11.6 µg/m³, while the 5-min average of in-traffic PM_2.5 concentration could be as high as 450 µg/m³. The in-traffic PM_2.5 concentrations were comparable to the data concurrently measured in a state-operated community monitoring site approximately 0.4 km away from the POE. Ozone (O₃) concentrations remained low in the region. The in-traffic 1-h NO₂ concentrations were below the 1-h NAAQS of 100 ppb, except for only two 5-min intervals. Our results suggest that traffic emissions do not result in elevated levels of pollutant concentrations at this POE compared to the concentration levels observed in the nearby community. In addition, the performance and accuracy of the low-cost sensors appear to be less reliable during our study, although the devices were capable of detecting the trends and variability in pollutant concentrations in real time.

Exposure to traffic-related air pollution is not merely linked to respiratory health issues but also poses significant risks to cardiovascular well-being. Individuals from lower-income communities residing in high-pollution zones are particularly vulnerable to adverse cardiorespiratory health impacts. Pollutants such as fine particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), and ozone (O₃) are recognized as a leading, yet preventable, contributor to cardiorespiratory diseases. Although research has extensively explored the short-term impact of these pollutants on respiratory health, the immediate effects on cardiovascular outcomes require further study. We explored associations of traffic-related air pollutants with airway inflammation, lung function, and cardiovascular health outcomes (metabolic syndrome [MetS]) collected from a sample of low-income participants (N = 662) from a US–Mexico border county. Airway inflammation was measured using exhaled nitric oxide tests (eNO), while lung function parameters were measured by spirometry. MetS risk factors (waist circumference, blood pressure, triglycerides, HDL, and fasting blood glucose) were also measured. While spirometry measures were negatively associated with air pollutants (p < 0.05), no associations were noted for eNO. We also found positive associations in linear and logistic models between air pollutants and obesity (BMI: p < 0.04; waist: p < 0.03), fasting blood glucose (p < 0.03), and metabolic syndrome (p < 0.04). These findings reaffirm the immediate adverse effects of air pollution on respiratory function and shed light on its broader metabolic consequences. Environmental and neighborhood conditions could potentially influence the associations with obesity. At the same time, the links between fasting glucose and metabolic syndrome might indicate underlying oxidative stress and systemic inflammation.

This project assesses the feasibility of employing mobile air pollutant concentration monitoring along fixed routes within an urban community to evaluate near-road exposure. Continuous mobile air monitoring measurements of four pollutants (PM_2.5, PM₁₀, NO₂, and O₃) were collected using high-quality air monitors paired with a GPS device to track coordinates and vehicle speed. Simultaneous near-road measurements of the same pollutants were taken at two stationary sites to establish correlations with the mobile air monitoring data. The results indicate that pollutant concentrations recorded by mobile air monitors align closely with those from near-road stationary sites. This study demonstrated strong concordance between mobile and stationary monitoring for particulate matter concentrations, with PM_2.5 and PM₁₀ showing high correlation coefficients (R² = 0.74 and 0.75, respectively). Ozone (O₃) exhibited particularly consistent spatial distributions across all measurement platforms—mobile, near-road, and community stationary sites—as reflected in even stronger correlations (R² = 0.93 and 0.89 for the two near-road sites). These robust associations suggest that mobile monitoring could serve as a viable alternative to stationary approaches for O₃ assessment. In contrast, nitrogen dioxide (NO₂) measurements displayed greater variability, with mobile concentrations consistently exceeding near-road stationary values and demonstrating weaker correlation (R² = 0.19), indicating potential limitations in mobile NO₂ monitoring reliability. This study highlights that mobile air pollutant monitoring in less congested communities can effectively capture exposure concentrations representative of both the community and near-road receptors represented by stationary air monitoring sites. Future research should explore how mobile air monitoring data can be utilized in exposure and health assessments, as well as how this technique can be applied in areas where stationary monitoring is impractical or prohibited due to cost or access limitations.