Nanoplastics, created by the fragmentation of larger plastic debris, are a serious pollutant posing substantial environmental and health risks. Here, we developed a polystyrene nanoparticle (PS-NP) exposure model during mice pregnancy to explore their effects on embryonic development. We found that exposure to 30 nm PS-NPs during pregnancy resulted in reduced mice placental weight and abnormal embryonic development. Subsequently, our transcriptomic dissection unveiled differential expression in 102 genes under PS-NP exposure and the p38 MAPK pathway emerged as being significantly altered in KEGG pathway mapping. Our findings also included a reduction in the thickness of the trophoblastic layer in the placenta, diminished cell invasion capabilities, and an over-abundance of immature red cells in the blood vessels of the mice. In addition, we validated our findings through the human trophoblastic cell line, HTR-8/SVneo (HTR). PS-NPs induced a drop in the vitality and migration capacities of HTR cells and suppressed the p38 MAPK signaling pathway. This research highlights the embryotoxic effects of nanoplastics on mice, while the verification results from the HTR cells suggest that there could also be certain impacts on the human trophoblast layer, indicating a need for further exploration in this area.

Global warming has profoundly affected the survival and development of human beings and has become a worldwide problem, and the mitigation of global warming requires a more accurate prediction of global temperature development. In order to accurately predict global temperatures in future years and provide feasible solutions, this paper comprehensively compares the prediction results of three prediction models. Firstly, a time series model is chosen to map the past time series to describe the global temperature changes. In addition, this paper selects three models for analysis by comparing a time series ARIMA model, a grey prediction model and a neural network model, fits the model using data information on the global average temperature for 200 consecutive years, and trains and corrects the model to obtain the projected global temperature for the next 80 years. Considering the advantages and disadvantages of each model and combining the conclusions of previous studies, this paper concludes that the neural network model predicts the most accurate results. Meanwhile, the results indicate that in the future, if the continuous increase in global temperature is not controlled, global warming will continue to cause irreversible damage to the Earth's ecosystem, which in turn will have a negative impact on human health.

In recent years, the rapid development of industrialisation has led to serious light pollution problems . In order to measure the degree of regional light pollution, firstly, this paper divides light pollution into three parts: ecological light pollution (Eco), daylight pollution (Dai) and decorative light pollution (Dec), selects nine secondary indicators, assigns a value to each indicator using the entropy weight method (EWM), and the three main indicators are 0.2484, 0.3569 and 0.3947, and combines with the tosis to establish a comprehensive evaluation model. In addition, considering the importance of necessary light, we added three metrics to the original metrics to optimise the model to ensure better safety and comfort.Finally, Applying the model to four specific areas, namely urban, rural, suburban and protected areas, to measure the specific pollution level of the region. Domestic research on light pollution is mainly qualitative research, this paper comprehensively considers the necessity of appropriate amount of light and the harm of excessive light, and proposes a comprehensive evaluation model of light pollution based on the entropy weight method to quantitatively analyse the degree of light pollution. This paper provides a new perspective for light pollution prevention and control.