分解长江经济带生产用水量、生活用水量时空差异的驱动效应,有利于用水总量控制目标的顺利实现。采用LMDI(Logarithmic Mean Divisia Index)方法,兼顾生产用水和生活用水,将用水总量时空差异分解为生产强度效应、产业结构效应、经济规模效应、生活强度效应和人口规模效应。结果显示:生产用水量是长江经济带及各省份用水总量变化的主要来源,生活用水量对用水总量的促增作用也逐渐增强;生产强度效应、产业结构效应是抑制用水总量增加的主要和次要因素,而经济规模效应、生活强度效应是促进用水总量增加的主要和次要因素,人口规模效应对用水总量的促增作用相对较弱;农业、工业经济增长都促进了用水总量增加,尤其是农业,农业、工业用水强度普遍下降及农业增加值所占比重下降,都促进了用水总量下降;生产用水量是各省份用水总量空间差异的主要来源,各省份用水总量与江苏、重庆空间差异的驱动因素存在差异性。因此,各省份应该贯彻落实高质量发展、转变经济增长方式,重点开展生产环节节水、兼顾生活环节,继续降低产业用水强度、优化升级产业结构,加强生活用水定额管理、提高节水意识,各省份可以以江苏、重庆为参考对象,依据用水总量空间差异驱动因素,充分挖掘可行的节水路径。 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