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China has undergone rapid industrialization and urbanization over the past 40 years. In this process, as a large country with a vast territory and a large population, China's population development and land utilization have been greatly affected and undergone dramatic changes. In this paper, we mainly discuss the temporal and spatial variation characteristics of land-use efficiency in China from 1991 to 2016 and the regional disparities and explore the impacts of demographic transition on land-use efficiency by employing a STIRPAT model. In terms of space, China's land-use efficiency has significant agglomeration distribution characteristics and regional inequality, and the degrees of agglomeration and differentiation have gradually become enhanced over time. Our study on the influences of demographic transition on land-use efficiency found a Kuznets curve relationship between the transition of population size and land-use efficiency, as well as between the income level transition and land-use efficiency. Especially, land-use efficiency first increases up to the population threshold of 10611.877&times, 104, then efficiency decreases as the population grows. The overall average population in the whole country is 4117.753&times, 104, which is smaller than the identified threshold. Interestingly, the factors influencing land-use efficiency also showed very significant regional disparities. In the eastern region, there is a U-curve relationship between the population employed in secondary industries (ES2) and land-use efficiency. Land-use efficiency decreases down to the ES2 threshold of 343.674&times, 104 for the eastern region, whereas the overall average ES2 is 874.976&times, 104, indicating that this region has reached the turning point where land-use efficiency will improve as the population employed in secondary industries increases. Meanwhile, the increase in the human capital level was significantly positively correlated with land-use efficiency in the eastern region. For the central region, the transition of the urban&ndash, rural population structure (measured by the urbanization rate) significantly increased land-use efficiency. In addition, the results of panel estimation showed a Kuznets relationship between the population employed in tertiary industries (ES3) and land-use efficiency in the western region. Land-use efficiency increases up to the ES3 threshold of 455.545&times, 104, and then decreases with an increasing population employed in tertiary industries, whereas the overall average ES3 in the western region is 415.97&times, 104, which is smaller than the identified threshold. Policymakers could use these findings to inform rational suggestions with a sound scientific basis regarding the promotion of land-use transition.

Increased atmospheric nitrogen (N) deposition significantly impacts N cycling in freshwater ecosystems. Relative to lakes, the importance of N deposition in riverine N load is less studied. Thus, this study monitored N deposition and riverine N load for three years and then used the export coefficient model to explore N deposition's contribution to riverine N load in a forest-dominated watershed. It is found that the annual export of total N (TN) deposition could explain 17.4%-19.2% of riverine TN load. The contribution of TN deposition to riverine TN load was significantly higher (P 0.05) during the crop production period (recorded as CPP, lasting from June to September, 22.7%) than the non-crop production period (Non-CPP, 13.8%). The application of chemical fertilizer and manure and the high precipitation were assumed as the primary reason for the increased N deposition and increased riverine TN load during CPP. This study shows that inland plain agriculture practices might considerably influence the nearby forest-dominated watershed, and it is necessary to develop sustainable agriculture programs for reducing riverine N load.

The simulation of landscape pattern optimization allocation (LPOA) to achieve ecological security is an important issue when constructing regional ecological security patterns. In this study, an LPOA model was developed by integrating a binary logistic regression model and a nonlinear programming model with a particle swarm optimization algorithm in order to consider the complexity of landscape pattern optimization in terms of the quantitative structure and spatial layout optimization, integrating the landscape suitability and factors that influence landscape patterns, and under constraints to maximize the economic, ecological, and comprehensive benefits of landscape patterns. The model was employed to simulate the LPOA in the Longquanyi District of Chengdu City, Sichuan Province, China. The model successfully obtained an appropriate combination of the landscape quantitative structure and spatial layout, as well as effectively integrating the landscape suitability and factors that influence the landscape pattern. Thus, the model addressed the problems of previous studies, such as neglecting the coupling between quantitative structure optimization and spatial layout optimization, ignoring the macrofactors that affect landscape patterns during optimization modeling, and initializing particles without considering the suitability of the landscape. Furthermore, we assessed and analyzed the accuracy and feasibility of the landscape pattern spatial layout optimization results, where the results showed that the overall accuracy of the optimization results was 84.98% with a Kappa coefficient of 0.7587, thereby indicating the good performance of the model. Moreover, the simulated optimization allocation scheme for the landscape pattern was consistent with the actual situation. Therefore, this model can provide support and a scientific basis for regional landscape pattern planning, land use planning, urban planning, and other related spatial planning processes.