Nitrous oxide (N₂O) is a potent greenhouse gas (GHG) with agricultural soils representing its largest anthropogenic source. However, the mechanisms involved in the N₂O emission and factors affecting N₂O emission fluxes in response to various nitrogenous fertilizer applications remain uncertain. We conducted a four-year (2012–2015) field experiment to assess how fertilization scheme impacts N₂O emissions from a rice-wheat cropping system in eastern China. The fertilizer treatments included Control (CK), Conventional fertilizer (CF), CF with shallow-irrigation (CF+SI), CF with deep-irrigation system (CF+DI), Optimized fertilizer (OF), OF with Urease inhibitor (OF+UI), OF with conservation tillage (OF+CT) and Slow-release fertilizer (SRF). N₂O emissions were measured by a closed static chamber method. N₂O emission fluxes ranged from 0.61 μg m^-2 h^-1 to 1707 μg m^-2 h^-1, indicating a significant impact of nitrogen fertilizer and cropping type on N₂O emissions. The highest crop yields for wheat (3515–3667 kg ha^-1) and rice (8633–8990 kg ha^-1) were observed under the SRF and OF+UI treatments with significant reduction in N₂O emissions by 16.94–21.20% and 5.55–7.93%, respectively. Our findings suggest that the SRF and OF+UI treatments can be effective in achieving maximum crop yield and lowering N₂O emissions for the rice-wheat cropping system in eastern China.

The prediction of soil organic matter is important for measuring the soil’s environmental quality and the degree of degradation. In this study, we combined China’s GF-6 remote sensing data with the organic matter content data obtained from soil sampling points in the study area to predict soil organic matter content. To these data, we applied the random forest (RF), light gradient boosting machine (LightGBM), gradient boosting tree (GBDT), and extreme boosting machine (XGBoost) learning models. We used the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) to evaluate the prediction model. The results showed that XGBoost (R² = 0.634), LightGBM (R² = 0.627), and GBDT (R² = 0.591) had better accuracy and faster computing time than that of RF (R² = 0.551) during training. The regression model established by the XGBoost algorithm on the feature-optimized anthrosols dataset had the best accuracy, with an R² of 0.771. The inversion of soil organic matter content based on GF-6 data combined with the XGBoost model has good application potential.

The public space environment of historical and cultural blocks is inseparable from human activities, which affects tourists’ behavior and perception activities. Through the evaluation of tourists’ environmental behavior perception, the relationship between spatial characteristics and tourists’ perception is fully considered, which plays an important role in the protection and development of public space in historical and cultural blocks. This paper takes the historical and cultural block of Sanhe Ancient Town in Hefei as the research area, focusing on the public space of the block. Through the analysis of the semantic differential method and eye movement legal quantitative analysis, from the angle of psychological perception and visual perception, we carried out an analysis of the historical and cultural block’s public space, and built the double sense of a comprehensive evaluation system of parsing the historical and cultural blocks with the whole situation of public space and the rule of the performance. The results show that: (1) the visual perception preference for spatial elements is in the order of architectural structure > green landscape > architectural decoration > commercial activities > participants > pavement > street furniture > others. (2) There is a significant correlation but not a complete convergence of psychological perception and visual perception. (3) Buildings, structures, and space formats play a key role in creating a sense of space scale, with the former playing a positive role and the latter a negative role. (4) The visual attraction of a green landscape is strong and can improve the visual quality of space. The research found that there are evaluation differences between the visual perception and psychological perception of spatial elements, which are significantly correlated but not fully convergent. Through quantitative analysis and the interpretation of tourists’ perception from different perspectives, we can take relevant improvement and optimization measures for many deficiencies of public space in historical and cultural districts.