Land use is an important carrier and intuitive result of urbanization process. Driven by the dual transformation of China’s land system and developed regional economy, the interrelationship between urbanization and land use non agriculturalization in coastal areas and its evolution are uniquely explored. Based on the county land use information of Zhejiang Province in 2005, 2010 and 2015, this paper quantitatively analyzes the differentiation of county comprehensive urbanization, land use non agriculturalization and the conversion source and flow of key county construction land in 2005-2015. Then use the Theil index and the bivariate spatial autocorrelation method to explore the spatial correlation model of urbanization level and land use non agriculturalization in Zhejiang Provinces. (1) The level of urbanization in Zhejiang County is rapidly increasing and gradually achieving spatial balance and forming a group like urbanization situation centered on Hangzhou, Ningbo,Jinhua, Wenzhou and other municipal districts; the focus of construction land changes from the central and northern plains to the southeast coastal plains. However, the increase in the municipal area is still the most obvious. The increase or decrease of land for agricultural conversion is the key reason for the large scale change in construction land in Zhejiang County; (2) The spatial positive correlation between land non agriculturalization and urbanization in Zhejiang Province has increased significantly, and the spatial differentiation situation has been highlighted. It has shown that the high aggregation area has shifted from the middle part to the east coast of Zhejiang and the islands. In general, the high high type is mostly distributed in the northern Zhejiang Plain, while the low low type extends from the coastal to the inland. (3) There is a significant scale effect of comprehensive urbanization and land use non agriculturalization in Zhejiang Province, and the correlation difference increases with spatial scale. This study reveals the spatial correlation between urbanization and land non agriculturalization in the period of urbanization of economically developed provinces. It has important guiding value for promoting the synergy of land use planning and urban planning, and implementing land transfer and trans administrative area replacement according to local conditions. 相似文献
The present study aimed to improve the performance of microbial fuel cells (MFCs) by using an intermittent connection period without power output. Connecting two MFCs in parallel improved the voltage output of both MFCs until the voltage stabilized. Electric energy was accumulated in two MFCs containing heavy metal ions copper, zinc, and cadmium as electron acceptors by connection in parallel for several hours. The system was then switched to discharge mode with single MFCs with a 1000-Ω resistor connected between anode and cathode. This method successfully achieved highly efficient removal of heavy metal ions. Even when the anolyte was run in sequencing batch mode, the insufficient voltage and power needed to recover heavy metals from the cathode of MFCs can be complemented by the developed method. The average removal ratio of heavy metal ions in sequencing batch mode was 67 % after 10 h. When the discharge time was 20 h, the removal ratios of zinc, copper, and cadmium were 91.5, 86.7, and 83.57 %, respectively; the average removal ratio of these ions after 20 h was only 52.1 % for the control group. Therefore, the average removal efficiency of heavy metal ions increased by 1.75 times using the electrons stored from the bacteria under the open-circuit conditions in parallel mode. Electrochemical impedance data showed that the anode had lower solution resistance and polarization resistance in the parallel stage than as a single MFC, and capacitance increased with the length of time in parallel.
With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance. 相似文献