Introduction: Due to the limitations of traditional didactic teaching, inquiry-based teaching has attracted increasing attention and has become an important content of curriculum teaching reform in college education. Nevertheless, it is vital to investigate students’ subjective acceptance of inquiry-based instruction and its influencing factors before inquiry-based teaching methods are widely implemented.Methods: In light of this, taking into account the psychological factors of students, an acceptance model of inquiry-based teaching pedagogy was established based on the extended technology acceptance model (TAM). Three additional variables, namely self-efficacy, implementation quality, and risk perception, were incorporated into the TAM. Firstly, subjective evaluation data of the influencing factors of inquiry teaching acceptance were obtained through a network questionnaire survey from university students in Guangdong, China, using snowball sampling and convenient sampling. A total of 485 valid questionnaires were retrieved, with an effective response rate of 88.2%. Then, internal consistency and reliability, convergent validity and discriminant validity of the model and its hypothesis were tested with reliability and validity tests. Finally, path analysis was used to examine key determinants of students’ acceptance of inquiry teaching and moderators.Results: Results indicated that the constructed model can explain the acceptability of inquiry teaching for college students by 88.6%; Attitude has a positive significant impact on behavioral intention; Perceived ease of use indirectly affects behavioral intention through perceived usefulness, while perceived usefulness indirectly affects behavioral intention through attitude; self-efficacy not only directly affects behavioral intention but also indirectly affects behavioral intention through implementation quality; implementation quality indirectly affects behavioral intention through perceived usefulness and attitude; students’ risk perception of inquiry-based teaching has no negative impact on behavioral intention.Conclusion: Overall, this study has implications for policymakers, teachers or learners in terms of the implementation and promotion of inquiry-based teaching in college classroom.

During the large-scale outbreak of COVID-19 in China, the Chinese government adopted multiple measures to prevent the epidemic. The consequence was that a sudden variation in residents’ travel behavior took place. In order to better evaluate the temporal distribution of air pollution, and to effectively explore the influence of human activities on air quality, especially under the special situation, this study was conducted based on the real data from a case city in China from this new perspective. Two case scenarios were constructed, in which the research before the changes of residents’ travel behavior was taken as case one, and the research after the changes in residents’ travel behavior as case two. The hourly real-time concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃ that have passed the augmented Dickey–Fuller (ADF) test were employed as a data source. A series of detailed studies have been carried out using the correlation method, entropy weight method and the Air Quality Index (AQI) calculation method. Additionally, the research found that the decrease rate of NO₂ concentration is 61.05%, and the decrease rate of PM₁₀ concentration is 53.68%. On the contrary, the average concentration of O₃ has increased significantly, and its growth rate has reached to 9.82%. Although the air quality in the first week with fewer travels was in the excellent category, and chief pollutant (CP), as well as excessive pollutant (EP), were not found, as traffic volume increased, it became worse in the second and third weeks. In addition to that, special attention should still be paid to the development trend of O₃, as its average hourly concentration has increased. The findings of this study will have some guiding significance for the study of air pollution prevention, cleaner production, and indoor environmental safety issues, especially for the study of abnormal traffic environments where residents’ travel behaviors have changed significantly.

The higher contribution of traffic delay to environmental impacts is urging the highway agencies to take work zone management into the maintenance schemes decision-making. Aiming to understand the role of work zone management in user CO₂ emissions reduction, this paper firstly developed a practical methodological framework of traffic delay-related CO₂ emissions caused by highway maintenance based on a popular life cycle user cost analysis approach in regard of the microscopic vehicle operation analysis. The method was applied in an actual freeway flexible pavement with 15-year design life in Shaanxi Province, China, covering three types of preventive maintenance, correction maintenance, and rehabilitation. In addition, the impacts of key inputs of proposed method on work zone user CO₂ emissions results were checked. The results show that traffic delay attributes to 29.4 of total CO₂ emissions of the life cycle of highway pavement maintenance, and 51.8 of work zone user CO₂ emissions result from preventive maintenance, especially from micro vehicle operations including speed change and queue near work zone (62 of total work zone user CO₂ emissions). The work zone management alternative strategies related to less traffic volume or higher highway capacity including vehicle type limitation and the limited work zone speed have an advantage in reducing the work zone CO₂ emissions over changing work zone length or work zone timing. The findings in this paper may present a useful tool and reference for robustly supporting the decision-making on highway maintenance carbon mitigation in work zone traffic.

This article explores the impact of weather and environment on shared bicycles. Using a random forest model combined with explanatory machine learning methods, the relationship, threshold effect, and interaction effect between weather factors and the transfer volume of shared bicycles at subway stations are analyzed. Research has shown that using the RF+IML method to study the impact of weather variables on shared bicycle transfer volume is feasible. There is a significant nonlinear relationship between various weather factors and shared bicycle transfers. Temperature, humidity, and rainfall have specific activation and threshold effects on the number of shared bicycle transfers. When humidity is below 60%, the variation in transfer volume remains relatively stable; however, once it exceeds 60%, the transfer volume drops sharply. When the temperature exceeds 17 °C, its impact tends to reach saturation. Similarly, when rainfall reaches around 20 mm, its adverse effect also approaches the threshold. Temperature is the most important factor affecting the prediction of shared bicycle transfer volume, with temperature, cold weather, and cold forecasts contributing over 35% to the total effect. The interaction effect between temperature and other weather factors accounts for 22% of the total effect.

To explore the effects of different traffic structures and wind speeds on concentration distribution of multi-component pollutants, CO, C₄H₈, and NO₂ with proportions of 81%, 12%, and 7%, respectively, were selected for computational fluid dynamics simulations. Their concentrations were estimated in a reference scenario and five contrast scenarios. When the number of cars is reduced by 15%, their concentrations at point 1 (–28, 1.5) and point 2 (28, 1.5) will decrease by more than 10%. When the number of cars is reduced by 25%, their concentrations will decrease by 20%. The largest concentration in 1.5 m above the ground level under six scenarios is measured on the leeward side when x=–3.3 m. Even if the wind speed increases to 3.0 m/s, the position where the largest concentration occurs remains the same. When wind speed increased to 3.0 m/s, the pollutants concentration on the leeward side decreased by an average of 4%, whereas that on the windward side decreased by an average of 11%. An additional 3.2%, 3.4%, 3.4%, 2.8%, 3.3%, and 3.1% reduction in CO concentration on the leeward side occurred. These results can help to strengthen the policy-making to create a good travel environment for pedestrians.