Decomposition analysis on direct material input and dematerialization of mining cities in Northeast China

Material dematerialization is a basic approach to reduce the pressure on the resources and environment and to realize the sustainable development. The material flow analysis and decomposition method are used to calculate the direct material input (DMI) of 14 typical mining cities in Northeast China in 1995-2004 and to analyze the demateri-alization and its driving factors in the different types of mining cities oriented by coal, petroleum, metallurgy and multi-resources. The results are as follows: 1) from 1995 to 2006, the increase rates of the DMI and the material input intensity of mining cities declined following the order of multi-resources, metallurgy, coal, and petroleum cities, and the material utilizing efficiency did following the order of petroleum, coal, metallurgy, and multi-resources cities; 2) during the research period, all the kinds of mining cities were in the situation of weak sustainable development in most years; 3) the pressure on resources and environment in the multi-resources cities was the most serious; 4) the petro-leum cities showed the strong trend of sustainable development; and 5) in recent years, the driving function of eco-nomic development for material consuming has continuously strengthened and the controlling function of material utilizing efficiency for it has weakened. The key approaches to promote the development of circular economy of min-ing cities in Northeast China are put forward in the following aspects: 1) to strengthen the research and development of the technique of resources' cycling utilization, 2) to improve the utilizing efficiency of resources, and 3) to carry out the auditing system of resources utilization.     

Impacts of soil fauna on litter decomposition at different succession stages of wetland in Sanjiang Plain, China

Litter decomposition is the key process in nutrient recycling and energy flow. The present study examined the impacts of soil fauna on decomposition rates and nutrient fluxes at three succession stages of wetland in the Sanjiang Plain, China using different mesh litterbags. The results show that in each succession stage of wetland, soil fauna can obviously increase litter decomposition rates. The average contribution of whole soil fauna to litter mass loss was 35.35%. The more complex the soil fauna group, the more significant the role of soil fauna. The average loss of three types of litter in the 4mm mesh litterbags was 0.3–4.1 times that in 0.058mm ones. The decomposition function of soil fauna to litter mass changed with the wetland succession. The average contribution of soil fauna to litter loss firstly decreased from 34.96% (Carex lasiocapa) to 32.94% (Carex meyeriana), then increased to 38.16% (Calamagrostics angustifolia). The contributions of soil fauna to litter decomposition rates vary according to the litter substrata, soil fauna communities and seasons. Significant effects were respectively found in August and July on C. angustifolia and C. lasiocapa, while in June and August on C. meyeriana. Total carbon (TC), total nitrogen (TN) and total phosphorus (TP) contents and the C/N and C/P ratios of decaying litter can be influenced by soil fauna. At different wetland succession stages, the effects of soil fauna on nutrient elements also differ greatly, which shows the significant difference of influencing element types and degrees. Soil fauna communities strongly influenced the TC and TP concentrations of C. meyeriana litter, and TP content of C. lasiocapa. Our results indicate that soil fauna have important effects on litter decomposition and this influence will vary with the wetland succession and seasonal variation. Foundation item: Under the auspices of State Key Development Program for Basic Research of China (No. 2009CB421103), Key Program of National Natural Science Foundation of China (No. 40830535/D0101), Knowledge Innovation Programs of Chinese Academy of Sciences (No. KZCX2-YW-BR-16, KSCX2-YW-N-46-06)     

福州市地表温度热点及时空变化分析

中国作为世界第一大发展中国家,近年来城镇化发展迅速,大量自然地表转化为人工地表,从而引起了一系列环境问题,其中以城市热岛问题最为显著。因此如何缓解因城市化进程的加快引起的城市热岛效应已成为热门研究方向。为精确分析城市空间格局对热集聚的影响,本研究利用2000年5月4日的Landsat ETM+和2016年7月27日获取的Landsat OLI两期遥感影像,获取福州市的土地覆盖信息并进行精度验证。在地表温度（Land Surface Temperature, LST）反演基础上通过热点分析（Getis-Ord Gi*）,并结合不透水面（Impervious Surface Area, ISA）信息来研究城市化进程中福州市16 年来 LST的变化特性,空间集聚特性及其产生的尺度效应。热点分析结果显示：① 通过分析福州市内各地和热点中心的距离与LST的关系可较好地反映空间热聚集。2000 年在距热点中心0.97、1.03、0.95 km范围内热聚集明显;2016 年则增长到分别在距热点中心半径1.89、2.01、2.10、2.05、2.13 km范围内热集聚显著且热点区数量也从3 个增加至5 个。热集聚区（热点区和较热区）总面积在此期间从15.7%增至47.3%;② 由于热点图中的热点区和冷点区的形成不单取决于LST的高低,因此热点分析与空间自相关分析方法相比,能更直观地分析土地覆盖变化对LST的影响,了解城市内部热强度变化的细节。本研究采用的热点分析方法可用于城市环境保护与规划,将来还可作为城市土地规划与热环境影响的分析依据。同时可利用热点分析图模拟城市微气候,估算城市绿地降温程度等。此外,未来还可基于此进一步探讨更多时相以及不同城市的对比分析,特别是对不同城市类型如带状城市,多中心城市及中心城市等的研究。     

O2和H2O对脉冲电晕分解NO的影响

在脉冲电晕放电条件下，探讨了O2和H=O对NO分解反应过程的影响，并对复杂组分中NO的分解特性进行了实验研究。研究结果表明，脉冲电晕放电对NO分解反应具有一定的促进作用，可有效地分解模拟烟气中的NO，脱除效率随脉冲电压的增加而缓慢增加；受O2和H2O影响，NO分解转化率较N2氛围略有降低。     

利用小波变换进行遥感多光谱图像融合的算法及实现

在分析了小波变换的分解与重建方法后,提出了一种基于区域的图像增强算法。先提取出源图的边缘,以图像的边缘为参考,围绕边缘建立融合窗口,然后结合区域内的图像信息,应用基于窗口的融合规则进行融合处理。实验结果显示,融合后的图像综合了3幅源图像的不同特征,处理后的图像变得容易识别了。表明该方法保持了尽可能多的原始信息,算法简单,稳定性好,适合于多光谱遥感图像识别、医学成像等领域。     

O_2和H_2O对脉冲电晕分解NO的影响

在脉冲电晕放电条件下,探讨了O2和H2O对NO分解反应过程的影响,并对复杂组分中NO的分解特性进行了实验研究。研究结果表明,脉冲电晕放电对NO分解反应具有一定的促进作用,可有效地分解模拟烟气中的NO,脱除效率随脉冲电压的增加而缓慢增加;受O2和H2O影响,NO分解转化率较N2氛围略有降低。     

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经验模态分解法在地下水资料处理中的应用

利用经验模态分解法（EMD）对地震地下流体观测资料中典型干扰信号的处理及趋势性变化的提取,结果表明,经验模态分解方法能有效地对信号进行分解,是一种无需预设带宽的自适应高通滤波方法,适用于地下流体观测资料的分析处理     

基于不等式约束的磁共振信号T2谱多指数分解法及算法改进

磁共振地下水探测信号的弛豫时间T₂和振幅与地下岩石孔隙大小和地下水的含水量直接相关。目前,磁共振地下水探测方法中,信号的衰减特性由平均弛豫时间T^*₂来描述。然而,对于复杂的多孔介质组成的岩石结构,仅由T^*₂无法准确描述地下孔隙情况。详细研究了基于不等式约束的多指数分解法,进行了信号T₂谱的提取,并重新计算多指数形式下的信号初始振幅。结果表明：对比信号在不同采集时间下的T₂谱提取结果,延长信号的采集时间有助于T₂谱的准确提取;通过磁共振仿真信号计算并分析不同信噪比下的T₂谱提取结果,信噪比大于30 dB时相对误差平均值小于5%;进行算法改进,得到信噪比大于10 dB时相对误差平均值小于5%的结果,且幅值和T₂的相对误差平均值降低了约1/3。由此可知:采用多指数分解法能够获得磁共振信号的T₂谱,正则化技术和加权修正能够提高T₂谱的准确度和抗干扰能力;与单指数方式相比,采用多指数拟合能够更准确地恢复信号初始振幅。     

时间尺度重构EEMD-GRNN改进模型预测PM2.5的研究

合理构建PM_2.5浓度预测模型是科学、准确地预测PM_2.5浓度变化的关键。传统PM_2.5预测EEMD-GRNN模型具有较好的预测精度,但是存在过于关注研究数据本身而忽略其物理意义的不足。本研究基于南京市2014-2017年PM_2.5浓度时间序列数据,分析PM_2.5浓度多尺度变化特征及其对气象因子和大气污染因子的尺度响应,基于时间尺度重构进行EEMD-GRNN模型的改进与实证研究。南京市样本数据PM_2.5浓度变化表现为明显的天际尺度和月际尺度,从重构尺度（天际、月际）构建GRNN模型更具有现实意义;同时,PM_2.5对PM₁₀、NO₂、O₃、RH、MinT等因子存在多尺度响应效应,以其作为GRNN模型中的输入变量更具有时间序列上的解释意义。改进后的EEMD-GRNN模型具有更高的PM_2.5浓度预测精度,MAE、MAPE、RMSE和R²分别为6.17、18.41%、8.32和0.95,而传统EEMD-GRNN模型的模型有效性检验结果分别为8.37、27.56%、11.56、0.91。对于高浓度天（PM_2.5浓度大于100 μg/m³）的预测,改进模型更是全面优于传统EEMD-GRNN模型,MAPE为12.02%,相较于传统模型提高了9.03%。