Mining activity in Italy has been one of the main productive activities for millennia, particularly in the Tuscany region which has a great mining tradition, unfortunately characterized in the past by a management lit...Mining activity in Italy has been one of the main productive activities for millennia, particularly in the Tuscany region which has a great mining tradition, unfortunately characterized in the past by a management little interest to environmental problems. The area under study is the disused mine Niccioleta, in Val d＇Aspra, located about 6 km NE of Massa Marittima in the province of Grosseto. The area is characterized by the presence of four major landfills, in which prevail quantitatively fine-grained materials resulting from the treatment by flotation of pyrite. The study of satellite images offers a new approach to the study of environmental problems. The results obtained from the RapidEye images showed the presence of pyrite and chalcopyrite followed from arsenopyrite, as confirmed by the analysis of diffractometer of the samples and by bibliographic data. RapidEye images lend themselves very to be used to monitor areas of disused mining deposits of ores with primary mineralization predominantly sulphides and subject to oxidized characterized by processes of oxidation/dissolution of pyrite sulphide most common and abundant. In fact, the results of this study have highlighted the potential of remote sensing applied to the study of mining areas, noting the possible benefits, both time and cost, which could be obtained by using these techniques.展开更多
为研究土壤重金属污染对作物生长尤其是根系生长的影响,探讨了利用遥感与作物生长模型同化方法获取水稻根重WRT(Weight of Root)的变化,进而动态监测水稻重金属污染胁迫的可行性。以吉林省长春市两块不同污染水平的水稻种植区为研究对象...为研究土壤重金属污染对作物生长尤其是根系生长的影响,探讨了利用遥感与作物生长模型同化方法获取水稻根重WRT(Weight of Root)的变化,进而动态监测水稻重金属污染胁迫的可行性。以吉林省长春市两块不同污染水平的水稻种植区为研究对象,以叶面积指数LAI(Leaf Area Index)为结合点,使用灰色关联度分析选择与根重关联度最高的作物参数CVR(干物质转化为根重的效率,Efficiency of Conversion into Roots),通过粒子群优化算法PSO(Particle Swarm Optimization)优化CVR,实现作物生长模型WOFOST(World Food Studies)与CCD遥感数据的同化,并用同化后的WOFOST模型模拟WRT进行水稻重金属污染胁迫状况分析,最后对研究区水稻重金属污染胁迫进行了分级评价。结果表明,整个生长期污染严重区域水稻根重比污染较轻区的水稻根重低,二者比值范围为0.894~0.972,均值为0.922,在水稻分蘖期比值最低达到0.894。可见根重的变化是监测水稻重金属污染胁迫的有效指标,该方法能够在水稻生长的早期(分蘖期)就监测到重金属污染胁迫。展开更多
文摘Mining activity in Italy has been one of the main productive activities for millennia, particularly in the Tuscany region which has a great mining tradition, unfortunately characterized in the past by a management little interest to environmental problems. The area under study is the disused mine Niccioleta, in Val d＇Aspra, located about 6 km NE of Massa Marittima in the province of Grosseto. The area is characterized by the presence of four major landfills, in which prevail quantitatively fine-grained materials resulting from the treatment by flotation of pyrite. The study of satellite images offers a new approach to the study of environmental problems. The results obtained from the RapidEye images showed the presence of pyrite and chalcopyrite followed from arsenopyrite, as confirmed by the analysis of diffractometer of the samples and by bibliographic data. RapidEye images lend themselves very to be used to monitor areas of disused mining deposits of ores with primary mineralization predominantly sulphides and subject to oxidized characterized by processes of oxidation/dissolution of pyrite sulphide most common and abundant. In fact, the results of this study have highlighted the potential of remote sensing applied to the study of mining areas, noting the possible benefits, both time and cost, which could be obtained by using these techniques.
文摘为研究土壤重金属污染对作物生长尤其是根系生长的影响,探讨了利用遥感与作物生长模型同化方法获取水稻根重WRT(Weight of Root)的变化,进而动态监测水稻重金属污染胁迫的可行性。以吉林省长春市两块不同污染水平的水稻种植区为研究对象,以叶面积指数LAI(Leaf Area Index)为结合点,使用灰色关联度分析选择与根重关联度最高的作物参数CVR(干物质转化为根重的效率,Efficiency of Conversion into Roots),通过粒子群优化算法PSO(Particle Swarm Optimization)优化CVR,实现作物生长模型WOFOST(World Food Studies)与CCD遥感数据的同化,并用同化后的WOFOST模型模拟WRT进行水稻重金属污染胁迫状况分析,最后对研究区水稻重金属污染胁迫进行了分级评价。结果表明,整个生长期污染严重区域水稻根重比污染较轻区的水稻根重低,二者比值范围为0.894~0.972,均值为0.922,在水稻分蘖期比值最低达到0.894。可见根重的变化是监测水稻重金属污染胁迫的有效指标,该方法能够在水稻生长的早期(分蘖期)就监测到重金属污染胁迫。
