Molten salts for efficient removal of radioactive contaminants from stainless steel surface: Mechanisms and applications.

Here, we have demonstrated an innovative decontamination strategy using molten salts as a solvent to clean stubborn uranium contaminants on stainless steel surfaces. The aim of this work was to investigate the evolutionary path of contaminants in molten salts to reveal the decontamination mechanism, thus providing a basis for the practical application of the method. Thermodynamic analysis revealed that alkali metal hydroxides, carbonates, chlorides and nitrates can react with uranium oxides (UO3 and U3O8) to form various uranates. Notably, the decontamination mechanism was elucidated by analyzing the chemical composition of the contaminants in the molten salts and the surface morphology of the specimens considering NaOH-Na2CO3-NaCl melt as the decontaminant. The decontamination process involved two stages: a rapid decontamination stage dominated by the thermal effect of molten salt, and a stable decontamination stage governed by the chemical reactions and diffusion of molten salt. Subsequently, a multiple decontamination strategy was implemented to achieve high decontamination rates and low residual radioactivity. Within the actual cleaning time of 30 min, the decontamination efficiency (DE) of UO3-contaminated specimens reached 97.8% and 93.0% for U3O8-contaminated specimens. Simultaneously, the radioactivity levels of all specimens were reduced to below the control level for reuse in the nuclear domain. Particularly, the actual radioactive waste from the nuclear industry reached a reusable level of radioactivity after decontamination. The NaOH-Na2CO3-NaCl melt outperforms conventional chemical solvents and may be one of the most rapid and efficient decontaminants for stubborn uranium contamination of metal surfaces, which provides insights in regard to handling nuclear waste.

Electrocatalytic and green system coupling strategy for simultaneous recovery and purification of uranium from uranium-containing wastewater.

The recycling of uranium in wastewater is not only beneficial to the protection of ecological safety but also has great significance for the sustainable development of nuclear energy. However, there is no satisfactory method to recover and reuse uranium efficiently up to now. Here, we have developed an efficient and economical strategy that can achieve uranium recovery and direct reuse in wastewater. The feasibility analysis verified that the strategy still had good separation and recovery ability in acidic, alkaline, and high-salinity environments. The purity of uranium recovered from the separated liquid phase after electrochemical purification was up to about 99.95%. Ultrasonication could greatly increase the efficiency of this strategy, and 99.00% of high-purity uranium could be recovered within 2 h. We further improved the overall recovery rate by recovering the residual solid-phase uranium, and the overall recovery of uranium was increased to 99.40%. Moreover, the concentration of impurity ions in the recovered solution met the World Health Organization guidelines. In summary, the development of this strategy is of great importance for the sustainable use of uranium resources and environmental protection.

Decontamination of uranium-containing radioactive waste from stainless steel surfaces using NaOH-based molten salts.

Herein, we report a new strategy for the rapid removal of uranium-containing contaminants from metal surfaces, and it relies on decontaminants made of NaOH-based molten salts. The addition of Na2CO3 and NaCl to NaOH exhibited superior decontamination performance, with a decontamination rate of 93.8% within 12 min, outdoing the performance of the single NaOH molten salt. The experimental results demonstrated that the synergistic effects between CO32- and Cl- promoted the corrosion efficiency of the molten salt on the substrate, which accelerated the decontamination rate. Additionally, benefiting from the optimization of the experimental conditions by the response surface method (RSM), the decontamination efficiency was improved to 94.9%. Notably, it also showed remarkable results in the decontamination of specimens containing different uranium oxides at low and high levels of radioactivity. This technology is promising for broadening the path in rapid decontamination of radioactive contaminants on metal surfaces.

The impacts of non-motorized traffic enforcement cameras on red light violations of cyclists at signalized intersections.

INTRODUCTION: With a significant increase in accidents involving cyclists, more attention has been paid to cycling safety. Previous studies on traffic accident revealed that red-light violations of non-motorized vehicles have become the leading cause of crashes at signalized intersections. The objective of this study is to investigate the impact of non-motorized traffic enforcement cameras (NTECs) on the red-light running behavior of cyclists, including ordinary e-bike riders, delivery e-bike riders, and bicyclists. METHOD: An observational study of 5,217 cyclists was conducted at six primary intersections in the downtown areas of Nanjing, China. A random parameter logit model was used to explore the safety effect of the NTECs and other factors related to red-light violation behavior. RESULTS: The results indicate higher reductions in red-light violations at intersections with the NTECs compared than at the non-adjacent intersections without the NTECs. Furthermore, the NTECs demonstrated a beneficial but smaller impact on the reduction of violations at adjacent intersections. Another primary finding was that the effects of the NTECs varied among three types of cyclists (ordinary e-bike riders, delivery e-bike riders, and bicyclists). CONCLUSIONS: The NTECs were found to be most effective in the case of delivery e-bike riders, followed by ordinary e-bike riders and bicyclists. In addition, the factors associated with the red-light violation behaviors of these three groups were also found to be different. In general, group size, maximum waiting time, waiting position, and visual search were significantly related to the probability of red-light violations in all three groups. PRACTICAL APPLICATIONS: Based on these findings, this study provides some feasible suggestions for improving the effect of the NTECs and for the future extension of the NTECs installation, such as the randomization of the enforcement and publicity campaigns.

Investigating factors influencing takeout shopping demand under COVID-19: Generalized additive mixed models.

The COVID-19 pandemic severely hampered the freedom of shopping travel while increasing individuals' interest in takeout. Although many studies have examined takeout shopping, the available literature provides insufficient evidence on the factors influencing takeout shopping demand under the COVID-19. In this study, generalized additive mixed models were developed based on sampling data of takeout orders in Nanjing before, during, and post the pandemic to measure the associations between takeout shopping demand and neighborhood characteristics at the business circle scale. The results show that population density, house prices, road density, and catering all have a significant impact on takeout shopping demand, while the roles of land use (residential and company indexes) before and post the pandemic are opposite. Besides, the factors influencing the recovery of the demand before and after the pandemic were analyzed. These findings provide important insights into the development of the takeout industry in the post-pandemic era.

Analysis of factors influencing delivery e-bikes' red-light running behavior: A correlated mixed binary logit approach.

The red-light running (RLR) behavior of delivery e-bike (DEB) riders in cities has become the primary cause of traffic accidents associated with this group at signalized intersections. This study aimed to explore the influencing factors of red light running behavior and identify the differences between the DEB riders and the ordinary e-bike (OEB) riders to aid the development of countermeasures. In this study, the mixed (random parameter) binary logistic model was employed to capture the effects of unobserved heterogeneity. With this approach, factors including individual characteristics, behavioral variables, characteristics of signalized intersections, and the traffic environment were examined. Additionally, to account for the combined influence on the RLR occurrence, mixed logit framework was developed to reveal the correlations among the random parameters. The data of e-bike riders' crossing behaviors at four signalized intersections in Xi'an, China were collected, and 3335 samples were recorded. The results indicated showed that DEB riders are more likely to run red lights than OEB riders. Factors that affect RLR behaviors of the two groups are different. Factors associated with the unobserved heterogeneity include red-light stage, observation time, age and waiting position of the rider. The joint influence among random parameters further illustrates the complexity of the contributing factors of riders' crossing behavior. Results from the models provide insights into the development of intervention systems to improve the traffic safety of e-bike riders at intersections.

The application and extension of the theory of planned behavior to an analysis of delivery riders' red-light running behavior in China.

Delivery riders, an occupation that has emerged from China's booming E-commerce industry, have attracted widespread attention due to their red-light running (RLR) and high accident rates. This study aimed to utilize the theory of planned behavior (TPB) to investigate the psychological characteristics of delivery riders' RLR intentions. A survey questionnaire was designed to collect data, including information regarding the extended variables, the basic components of the TPB and demographic characteristics. The survey was conducted in Xi'an, and 228 complete questionnaires were collected. Structural equation modeling was used to examine the data, and a multiple group analysis of the demographic variables was conducted. The results showed that the expanded TPB model had a better model fit and higher variance explanation than the original TPB model. Extended constructs, i.e., conformity tendency (CT) and the traffic environment (TE), were significant predictors, and attitude was the strongest predictor of all the examined variables related to RLR intentions. Finally, the path parameters of the expended TPB model were adapted for different demographic groups, and some differential effects were also found. These results could provide a basis for the design of intervention measures and safety education schemes by delivery platforms and traffic management departments to reduce RLR behavior among delivery riders.

Exposure assessment of cyclists to UFP and PM on urban routes in Xi'an, China.

With the promotion of bicycle sharing, cycling as an active transportation mode is a matter of public interest. However, cyclists' recurrent exposure to traffic-related air pollution is associated with the potential health risks. Quantification of the health risks associated with daily exposure of commuting cyclists to atmospheric pollutants is vital, but barely reported. In this study, real-time mobile measurement campaigns were performed with high time-resolution portable instruments, along two commuting routes in Xi'an, China. We investigated personal exposure and inhaled dose of particulate matter and ultrafine particle (UFP) for cyclists. The results showed cyclists' exposure to average pollutants concentrations: fine particulate matter (PM2.5, 38.6 ±â€¯17.1 µg m-3) and UFP (18,172 ±â€¯11,282 particles cm-3). The exposure "hotspots" of cyclists were identified: intersections, diesel engines, etc. Cyclists' exposure to the highest PM2.5 (46.9  µg m-3) concentrations were observed in morning periods; these were ∼36%/42% higher compared to the afternoon or evening, while the latter periods corresponded to higher UFP concentrations (18,342/18,502 particles cm-3). The measurements of PM2.5 and UFP were clearly higher during autumn months, when compared to summer months. In multivariate models, wind speed was not significant, temperature and local urban background concentrations explained 70.9% the variation of PM2.5, the 67.8% of UFP was explained by temperature, traffic and relative humidity, and each 100 increase in on-road vehicles were associated with increase of 1328 particles cm-3 for UFP exposure in cyclists. Cycling in bike boulevards decreased exposure concentrations by 31.5% for PM and 36.6% for UFP compared to traffic roadsides, moving vehicles were identified as key contributors to PM0.25-0.3 and PM2.0-10 of cyclists' exposure. The potential health risks deserve attention under the mobility and air pollution challenges faced by many metropolitan areas in emerging economies. Our findings could serve to promote better design for low-exposure network of separated bike boulevards.