RESUMO
Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences > total consumption of social goods > number of private cars > permanent population > freight turnover > urban green space area > transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to promote the sustainable development of the transportation industry.
RESUMO
Cadmium (Cd) is a toxic metal that poses serious threats to human health. Rice is a major source of dietary Cd but how rice plants transport Cd to the grain is not fully understood. Here, we characterize the function of the ZIP (ZRT, IRT-like protein) family protein, OsZIP2, in the root-to-shoot translocation of Cd and intervascular transfer of Cd in nodes. OsZIP2 is localized at the plasma membrane and exhibited Cd2+ transport activity when heterologously expressed in yeast. OsZIP2 is strongly expressed in xylem parenchyma cells in roots and in enlarged vascular bundles in nodes. Knockout of OsZIP2 significantly enhanced root-to-shoot translocation of Cd and alleviated the inhibition of root elongation by excess Cd stress; whereas overexpression of OsZIP2 decreased Cd translocation to shoots and resulted in Cd sensitivity. Knockout of OsZIP2 increased Cd allocation to the flag leaf but decreased Cd allocation to the panicle and grain. We further reveal that the variation of OsZIP2 expression level contributes to grain Cd concentration among rice germplasms. Our results demonstrate that OsZIP2 functions in root-to-shoot translocation of Cd in roots and intervascular transfer of Cd in nodes, which can be used for breeding low Cd rice varieties.
RESUMO
Over the past decade, our understanding of the impact of air pollution on short- and long-term population health has advanced considerably, focusing on adverse effects on cardiovascular and respiratory systems. There is, however, increasing evidence that air pollution exposures affect cognitive function, particularly in susceptible groups. Our study seeks to assess and hazard rank the cognitive effects of prevalent indoor and outdoor pollutants through a single-centre investigation on the cognitive functioning of healthy human volunteers aged 50 and above with a familial predisposition to dementia. Participants will all undertake five sequential controlled exposures. The sources of the air pollution exposures are wood smoke, diesel exhaust, cleaning products, and cooking emissions, with clean air serving as the control. Pre- and post-exposure spirometry, nasal lavage, blood sampling, and cognitive assessments will be performed. Repeated testing pre and post exposure to controlled levels of pollutants will allow for the identification of acute changes in functioning as well as the detection of peripheral markers of neuroinflammation and neuronal toxicity. This comprehensive approach enables the identification of the most hazardous components in indoor and outdoor air pollutants and further understanding of the pathways contributing to neurodegenerative diseases. The results of this project have the potential to facilitate greater refinement in policy, emphasizing health-relevant pollutants and providing details to aid mitigation against pollutant-associated health risks.