Changes in traffic congestion and air pollution due to major roadway infrastructure improvements in Texas

Roadway construction as a natural experiment to examine air pollution impacts on infant health

Traffic-related air pollution (TRAP) poses a significant public health risk that is associated with adverse birth outcomes. Large roadway infrastructure projects present a natural experiment to examine how resulting congestion change is associated with adverse birth outcomes for nearby populations. This study is designed to examine the influence of living close to a roadway before, during, and after a construction project using a difference-in-differences design. We integrated data on all large roadway construction projects (defined as widening of existing roads, building new roads, improving bridges, installing intelligent transportation systems, improving intersections, and installing or upgrading traffic signals) in Texas from 2007 to 2016 with Vital Statistic data for all births with residential addresses within 1 km of construction projects. Our outcomes included term low birth weight, term birth weight, preterm birth, and very preterm birth. Using a difference-in-differences design, we included births within 3 years of construction start and 2 years of construction end. In our main model, the exposed group is limited to pregnant individuals residing within 300 m of a construction project, and the control group includes those living within 300-1000 m from a project. We used regression models to estimate the influence of construction on infant health. We included 1,360 large roadway construction projects linked to 408,979 births. During construction, we found that the odds of term low birth weight increased by 19% (95% CI: 1.05, 1.36). However, we saw little evidence of an association for other birth outcomes. Contrary to our hypothesis of decreased TRAP after construction ends, we did not observe consistent improvements post-construction for pregnant individuals living within 300 m. Continued consideration of the influence of traffic congestion programs on birth outcomes is necessary to inform future policy decisions.

Energy-saving and CO2 reduction strategies for new energy vehicles based on the integration approach of voluntary advocacy and system dynamics

The low-carbon development of new energy vehicles (NEVs) is critical to achieving the goals of carbon peaking and carbon neutrality. As such, combining gray model theory with system dynamics (SD-GM) and based on the bidirectional-cycle prediction theory, we propose a NEV annual average mileage algorithm considering the impact of the epidemic in China, taking private cars as an example. Then, combining a voluntary advocacy strategy (VA) with the SD-GM theory (VA-SD-GM integration), we establish an energy-saving and carbon-reduction management model. To evaluate the proposed algorithm, we performed a dynamic simulation. The results indicate that the enhanced green scenario enabled significant energy-saving and CO2 reduction performance but would cause side effects in the long term. Compared with the enhanced green scenario, the linkage mode reduced the impact of parking space tension, the number of NEV trips, and the intensification of traffic congestion by approximately 33%, 50%, and 34%, respectively. It effectively suppressed the continuous increase in side effects and had a synergistic effect of carbon reduction, energy conservation, congestion alleviation, and side-effect reduction. The study provides a theoretical basis for optimizing the energy-saving and CO2 reduction path of NEVs.