Environmental problems caused by extraction of minerals have long been a focus on environmental earth sciences. Vegetation growing conditions are an indirect indicator of the environmental problem in mining areas. A growing number of studies in recent years made substantial efforts to better utilize remote sensing for dynamic monitoring of vegetation growth conditions and the environment in mining areas. In this article, airborne and satellite hypersectral remote sensing data—HyMap and Hyperion images are used in the Mount Lyell mining area in Australia and Dexing copper mining area in China, respectively. Based on the analyses of biogeochemical effect of dominant minerals, the vegetation spectrum and vegetation indices, two hyperspectral indices: vegetation inferiority index (VII) and water absorption disrelated index (WDI) are employed to monitor the environment in the mining area. Experimental results indicate that VII can effectively distinguish the stressed and unstressed vegetation growth situation in mining areas. The sensitivity of VII to the vegetation growth condition is shown to be superior to the traditional vegetation index—NDVI. The other index, WDI, is capable of informing whether the target vegetation is affected by a certain mineral. It is an important index that can effectively distinguish the hematite areas that are covered with sparse vegetation. The successful applications of VII and WDI show that hyperspectral remote sensing provides a good method to effectively monitor and evaluate the vegetation and its ecological environment in mining areas.
为实现自养生物脱氮,采用膨胀颗粒污泥床小试反应器(R1～R3)培养全程自养脱氮工艺(Completely Auto-trophic Nitrogen Removal Over Nitrite,CANON)颗粒污泥,考察了不同上升流速、水力剪切力和溶解氧浓度下CANON颗粒污泥浓度和粒径的变化规律.结果表明:系统稳定后,污泥浓度呈现出R2＞R1＞R3,R1、R2成功实现CANON颗粒污泥启动,并且实现了颗粒化,而R3中较高的上升流速、水力剪切力导致系统内污泥易碎裂从而洗出系统,导致R3未能启动成功.
为贯彻落实四川省"一干多支、五区协同"发展战略,支持川西北生态示范区加强生态环境保护,创造更多生态产品,发展高原特色产业,实现优势区域更好发展、生态功能区更好保护.立足川西北生态示范区发展定位,梳理分析其自然生态状况和特色优势资源,围绕"什么是生态产品、怎么创造生态产品、创造什么样的生态产品"的研究思路,提出8种适合川西北生态示范区发展的生态产品模式及其实现路径,以期为实现川西北生态示范区生态环境保护和经济社会发展的有机统一提供参考.
为研究郪江流域遂宁段水环境质量,对郪江干流和8条支流开展了现场调查和定期监测.采用内梅罗综合污染指数、断面类别比例法和水环境质量指数对流域水质进行了综合分析.利用综合营养状态指数对水源地水体富营养化进行了评价.结果表明:郪江流域遂宁段水质不能够达到《地表水环境质量标准》(GB3838-2002)Ⅲ类标准要求,水质为轻度污染,枯、平水期水质优于丰水期.支流水质较差,加重了干流水体负荷.流域部分饮用水源地为Ⅳ类水质,同时表现为轻度富营养化.研究成果为水质改善提供理论依据和水生态环境质量稳定达标提供技术支撑.
为增强环卫智能化程度,利用TensorFlow深度学习框架实现人工神经网络(Artificial Neural Network,ANN)算法,建立以生活垃圾图像RGB均值和垃圾重量为输入的生活垃圾组分识别模型.对比不同超参数设置下模型A1至A4的识别效果.结果表明,使用三层ANN网络结构和Adam优化算法可以建立性能优秀的垃圾组分识别模型.A1至A4模型经过参数训练后识别误差均在±5％内.对A1至A4模型的学习率和迭代次数进行调试可得,A4模型即隐藏层第一层、隐藏层第二层和输出层的激活函数分别为ReLU函数、Tanh函数和Sigmoid函数的ANN模型,在学习率为0.01的情况下,识别误差在±1％以内,稳定性最佳.
