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The transportation industry plays a key role in reducing urban emissions of air pollutants and energy consumption. The transition from traditional fossil fuel-based vehicles (TFFBVs) to new energy vehicles (NEVs) is critical to China's strategic goal of reaching peak carbon dioxide (CO2) emissions before 2030 and achieving carbon neutrality before 2060. On the basis of the environmental status and development of NEVs in Chongqing in 2020, we designed scenarios for replacing TFFBVs in Chongqing with NEVs according to targets such as the number of proposed NEVs in China's 14th Five-Year Plan. Following this, we evaluated the environmental and health benefits of NEVs and their monetary value using exposure–response and disease–cost methods. Replacing 18%, 35%, and 50% of TFFBVs with NEVs can create health benefits of approximately CNY 11.391 billion, CNY 21.696 billion, and CNY 30.443 billion, accounting for 4.56%, 8.68%, and 12.18%, respectively, of Chongqing's GDP in 2020. These amounts exceed the cost of government subsidies. Greater health benefits were derived from reducing the toxic emissions of nitrogen dioxide (NO2); the reduction in deaths caused by cardiovascular diseases created the best benefits for health endpoints, exceeding a 59% reduction in all three scenarios. Our study provides empirical support for promoting NEVs. [ABSTRACT FROM AUTHOR]

The tensile force on the hanger cables of a suspension bridge is an important indicator of the structural health of the bridge. Tensile force estimation methods based on the measured frequency of the hanger cable have been widely used. These methods empirically pre-determinate the corresponding model order of the measured frequency. However, because of the uncertain flexural rigidity, this empirical order determination method not only plays a limited role in high-order frequencies, but also hinders the online cable force estimation. Therefore, we propose a new method to automatically identify the corresponding model order of the measured frequency, which is based on a Markov chain Monte Carlo (MCMC)-based Bayesian approach. It solves the limitation of empirical determination in the case of large flexural rigidity. The tensile force and the flexural rigidity of cables can be calculated simultaneously using the proposed method. The feasibility of the proposed method is validated via a numerical study involving a finite element model that considers the flexural rigidity and via field application to a suspension bridge. [ABSTRACT FROM AUTHOR]