On-road carbon dioxide (CO₂) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to investigate the on-road CO₂ emission characteristics with a portable emission measurement system (PEMS) and a global position system (GPS). A deep learning-based LDDT CO₂ emission model (DL-DTCEM) was developed based on the long short-term memory network (LSTM) and trained by the measured data with the PEMS. Results show that the vehicle speed, acceleration, VSP, and road slope had obvious impacts on the transient CO₂ emission rates. There was a rough positive correlation between the vehicle speed, road slope, and CO₂ emission rates. The CO₂ emission rate increased significantly when the speed was >5 m/s, especially at high acceleration. The correlation coefficient (R²) and the root mean square error (RMSE) between the monitored CO₂ emissions with PEMS and the predicted values with the DL-DTCEM were 0.986–0.990 and 0.165–0.167, respectively. The results proved that the model proposed in this study can predict very well the on-road CO₂ emissions from LDDTs.

Microplastics (MPs) are receiving increasing attention because of their potential harm to the environment and human health. This research aims to summarize the abundance, toxicological effects, and analysis methods of MPs, as well as present their current status and trends in scientific research. Bibliometric analysis confirmed a substantial rise in annual research papers on MPs, predominantly over the previous nine years. The central research areas relating to MPs include distribution, sources, toxic effects, analytical approaches, and adsorption of MPs with other pollutants. Airborne MPs are a primary source of microplastic pollution in remote areas. Humans may inhale and ingest MPs, leading to the accumulation of these particles in their bodies. Additionally, microplastics can have biological toxicity that poses a potential threat to human health. Standard procedures for sampling and both qualitative and quantitative analysis of microplastics in various environmental media must be established urgently to enable effective comparison of experimental conclusions.

A cross-sectional study based on the community was conducted to explore the association between sleep status and LUTS among middle and old-aged men. Male residents in Zhengzhou aged 40 or older were recruited. Participants received the Pittsburgh Sleep Quality Index questionnaire and the International Prostate Symptom Score questionnaire to evaluate sleep status and the severity of lower urinary tract symptoms (LUTS), respectively. Logistic regression analyses and linear regression analyses were performed to evaluate the relationship between sleep quality and sleep duration and LUTS. A total of 5785 participants were enrolled. Multivariable analyses showed a positive relationship between sleep quality and LUTS (β 0.716, 95% CI 0.647–0.784), and poor sleepers were significantly associated with moderate or severe LUTS (OR 2.486, 95% CI 2.095–2.950). U-shaped dose–response relationship revealed that sleeping less than 5.8 h/day and more than 7.9 h/day was related to moderate or severe LUTS and more than 7.9 h/day of sleep duration was associated with poor voiding and storage symptoms (P for nonlinearity < 0.001). Similar relationship was observed between sleep status and nocturia. It showed a significantly positive relationship between sleep quality and LUTS. U-shaped dose–response relationships between sleep duration and LUTS were observed.