南京城郊农业土壤重金属污染的遥感地球化学基础研究
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摘要
土壤重金属污染一直是环境地球化学高度关注的研究热点。最近几年,随着各种微量、超微量和微区分析技术的引入,重金属元素分析精度不断提高,检出限不断降低,这些都从信息维促进了土壤重金属污染的研究。然而在时空维方面,目前土壤重金属污染监测仍依赖传统地球化学方法,即野外采样,然后进行室内化学分析。该方法存在费时、费钱等缺点,不适合大范围监测。对于污染形式日益严重的发展中国家来说,由于资金短缺,困难更大。为此,寻找一种快速、节省的土壤重金属污染监测方法就势在必行。
遥感地球化学能够动态、快速、宏观地获取地表地球化学信息,已广泛应用于环境地球化学和土壤科学等领域。然而,目前利用遥感地球化学方法研究农业土壤重金属污染的研究尚未见报道。这其中最主要的困难在于重金属元素在土壤中含量甚微,现有技术难以直接探测到重金属元素光谱信息。探究土壤重金属元素光谱响应,研究土壤光谱与重金属元素关系是当前遥感地球化学面临的难点之一。
城郊土壤密切接触密集的城市人群,涉及众多生命的健康和安全。严重的重金属污染是城郊农业土壤的一个重要特征。城郊农业土壤污染一方面影响土壤的生态功能,同时污染物通过食物链传递和土壤颗粒物直接吸入而影响城市居民的健康,城郊农业土壤污染已成为公众普遍关注的问题。江宁和八卦洲是南京城南和城北的两处城郊地区,它们是南京重要的粮食和蔬菜供应基地,与南京居民的健康和安全息息相关。
基于以上背景,本文在江苏省地质调查研究院“江苏省国土生态地球化学调查”项目的支持下,选取江宁和八卦洲这两个受人类活动影响较强、且对于工农业生产具有重要位置的地区作为研究区,研究遥感地球化学方法预测农业土壤重金属元素含量可行性,为遥感技术快速制图土壤重金属污染提供理论依据和技术方法途径。本研究一方面可以促进土壤重金属污染研究的时间维和空间维,为土壤重金属污染监测、评价与制图快速、经济地提供信息;另一方面又拓宽了遥感地球化学研究范畴,促进遥感地球化学这门新兴学科的发展。此外,本研究可以为今后研究区的污染控制、风险评估、土地利用规划以及政府决策等提供必要信息,有助于提高粮食安全和维护居民健康,促进区域可持续发展。
遥感地球化学是一门综合性学科,而其基础理论、技术方法和应用又都包罗万象,本论文将研究重点着眼于基础研究。电磁波与地物的相互作用是遥感地球化学探测地表化学成分的基础。土壤光谱特征是遥感地球化学方法快速识别土壤化学成分的关键。利用各种分析技术和方法,努力将土壤光谱和重金属元素结合起来,建立土壤重金属元素与土壤光谱的关系,是本论文最基本目的。
本文以土壤和重金属元素光谱特征为主线,以建立土壤光谱与重金属元素关系为目标,围绕土壤重金属元素的光谱响应进行了系统的探索研究。在研究土壤光谱与重金属元素的关系之前,首先利用传统地球化学方法调研了江宁和八卦洲土壤重金属元素分布特征及它们的化学形态,并基于GIS技术,利用地统计学和空间自相关方法研究了它们的空间变异性。明了重金属元素以及土壤的光谱特征,是探索遥感地球化学方法快速预测土壤重金属元素可行性的前提。为此,本文测试了重金属元素以及南京城郊土壤的反射光谱,进而利用室内模拟污染试验,研究了不同重金属污染级别土壤的反射光谱响应。在此基础上,对江宁和八卦洲土壤反射光谱和重金属元素的关系进行了研究,利用反射光谱对这两个地区土壤重金属元素含量进行了预测,并探讨了反射光谱预测重金属元素的机理。除了反射光谱,遥感地球化学可利用的谱段还有很多,适合于地球遥感的热红外波段在土壤重金属污染研究中能否发挥作用,论文进而对此进行了研究。通过对比反射光谱与热红外光谱的研究结果,从而为遥感地球化学监测土壤重金属污染选择最佳监测波段。基于该结果,在当前难以获取研究区合适遥感影像情况下,通过室内模拟传感器波段反射率预测了南京城郊土壤重金属元素含量,并为今后利用遥感技术快速、大范围制图土壤重金属污染传感器的选择提供了理论依据。论文主要结论和认识如下:
(1)长江流域贯穿全流域的Cd异常是最近几年发现的新问题,目前已引起国土资源部及沿江各省的高度重视。本文将地统计学和化学形态分级提取方法联合用于Cd的来源和污染风险研究,得出了一些较为新颖的结论。首先空间自相关研究表明Cd的空间自相关性很小,说明Cd的空间分布较为零乱,这也暗示Cd的异常可能受到了人为的影响。进而化学形态分析结果表明,Cd以有效态为主,平均占其总量的82.3%。考虑到Cd总量已达轻微污染程度,说明八卦洲地区存在严重的Cd污染风险。此外相关分析表明,残渣态Cd所占比率与总Cd呈负指数关系,这进一步说明八卦洲地区Cd高含量与人类活动有关。
(2)详细研究了土壤重金属元素的反射光谱响应。指出在土壤中,只有具有特征吸收峰的重金属元素,而且当它们的含量相当高时(如Cr和Cu含量为4000 mg/kg)方可表现出自身光谱行为。对于农业土壤来说,很少达到如此高污染。因此本研究指出,直接从光谱吸收特征角度考虑,使用反射光谱快速识别并预测农业土壤重金属元素含量不是可行之策,利用反射光谱预测土壤重金属元素含量必须另辟蹊径。
(3)首次利用反射光谱成功预测了农业土壤重金属元素含量,并对预测机理进行了解释。研究结果表明,无论江宁还是八卦洲,土壤重金属元素与反射率都呈负相关。重金属元素预测精度顺序与它们和铁的相关性顺序一致,即与铁相关性高的元素预测精度也高,与铁相关性低的元素预测精度也低。重金属元素与土壤铁的内部相关是反射光谱预测无光谱特征重金属元素的机理。
(4)除了预测农业土壤重金属元素含量,本文还利用反射光谱识别了受工业严重污染的土壤,并建立了工业污染土壤的光谱识别标准:总体反射率低于30%,880nm附近吸收峰深度大于2%,以及粘土矿物在2210nm附近吸收峰深度小于4%。反射光谱法是对传统土壤重金属污染源识别方法的一种补充。
(5)利用反射光谱成功识别了土壤针铁矿,并提出了识别土壤针铁矿的光谱标识,即针铁矿电子对跃迁2(4T1g)所产生的490nm附近双吸收峰是土壤针铁矿的诊断性光谱特征。通过单一土壤的模拟试验以及对不同土壤的研究,结果表明土壤在490nm波段处吸收峰深度随土壤针铁矿含量增加而加深,二者具有很好的相关关系,可以利用该吸收峰深度快速预测土壤针铁矿含量。
(6)针铁矿对重金属元素的吸附是影响重金属元素在土壤中含量的重要因素。本文进行了针铁矿对重金属元素的吸附研究。结果表明,针铁矿对Ni2+的等温吸附曲线呈“Langmuir”型,在浓度较低时,吸附量随平衡浓度的增加而迅速升高,当平衡浓度达到一定程度时,吸附量也趋于饱和。反射光谱、XPS和FT-IR研究结果表明,针铁矿对Ni2+的吸附为专性吸附,吸附时Ni2+置换针铁矿A型羟基中的H+,Ni2+作为释电子的Lewis碱,针铁矿作为受电子的Lewis酸,电子从Ni2+向针铁矿中的氧和铁转移。
(7)热红外研究结果表明,南京城郊农业土壤的热红外光谱主要反映了粘土矿物OH伸缩振动和Si-O键伸缩和弯曲振动,而与重金属元素有关的有机质和铁氧化物信息则不明显。此外,人为污染模拟试验结果表明,土壤热红外光谱也难以直接显示重金属元素信息。可见,无论是直接还是间接方式,热红外波段都不如可见光近红外(VNIR)波段对重金属元素敏感,因此,本文认为监测土壤重金属污染的最佳波段是VNIR波段。
(8)基于上述最佳波段选择结果,在当前难以获取研究区合适遥感影像情况下,利用实验室模拟传感器波段预测了研究区土壤重金属元素含量。通过对不同平台、不同光谱和空间分辨率传感器HyMap、TM以及Quickbird的模拟,结果表明土壤重金属元素可以被这三种传感器预测,而且它们的预测精度非常接近,说明光谱分辨率不是预测重金属元素含量的关键性因子。综合考虑花费以及混合像元影响,本研究认为Quickbird是快速制图本区农业土壤重金属污染的首选传感器。
Heavy metal pollution has been an important problem in the field of environmental geochemistry all along. Due to the application of modern analytical technologies and computer technology, the analytical accuracies of heavy metals become higher and higher and the analytical limits of detection become lower and lower, which contribute to the research of heavy metal pollution in the area of information dimension. However, the research of soil heavy metal pollution in the area of spatial-temporal dimension still relies on the conventional geochemical methods, which are based on the raster sampling and chemical analysis and hence are time-consuming and relatively expensive. Therefore, it is urgent to find a rapid and cost-effective way for investigating soil heavy metal pollution.
The remote-sensing geochemistry approach, which can allow for synoptic and repetitive coverage of large areas, has been widely used in the area of environmental geochemistry and pedology. To our knowledge, however, until now examples of the use of remote-sensing geochemistry approach investigating heavy metals pollution for agricultural soils have not been reported. The major difficulty is that the contents of heavy metals in soils are very low, and hence it’s very difficult to detect their spectral features with the existing technologies. It is one of the difficulties to explore the spectral responses of heavy metals in soils and research the relationships between the soil spectra and heavy metals.
Soils in suburban environment come easily in contact with humans and hence have an important influence on public health. Serious heavy metal pollution is one of the important properties of many suburban soils. The heavy metal pollution in the suburban soils can affect the ecological functions of soils. On the other hand, the pollutants can influence the townsman’s health by means of food chain or the inhalation of soil particles. Therefore, the problem of heavy metal pollution in suburban agricultural soils has come to attention by the public. Jiangning and Baguazhou areas are located in the south and north of the Nanjing city, respectively. Both areas are the important base for food and vegetable supply of Nanjing, and hence they are closely linked to the people’s health.
Based on the background mentioned above and supported by the project named“biogeochemical survey for the land of Jiangsu province”, which was funded by Geological Survey of Jiangsu Province, the two important areas, Jiangning and Baguazhou, were chosen as the study areas to research the feasibility of the remote sensing approach for predicting heavy metal pollution in agricultural soils. One side, this study can contribute to the research of soil heavy metal pollution in the area of spatial-temporal dimension. On the other hand, this study can widen the research scope of remote-sensing geochemistry, and consequently promote the developments of the new subject, remote-sensing geochemistry. Moreover, this study has the function of enhancing the food security, protecting people’s health, and advancing the sustainable development of Nanjing area.
Remote-sensing geochemistry is a kind of comprehensive subject, the basal theory, technologies and applications of which are extensive. This study mostly focused on the basal research. Soil spectral features are the basis for the identification of soil constituents using the remote-sensing geochemistry approach. The primary objective of this study was to try to find the relationships between soil spectra and soil heavy metals using all kinds of technologies and methods.
In this dissertation, the spectral features of soils and heavy metals were the main line, and building the relationships between soil spectra and the contents of heavy metals was the major aim. Based on these objects, the spectral responses of soil heavy metals were systemicly researched. Before the relationships between soil reflectance spectra and soil heavy metals were explored, the contents and chemical speciations of heavy metals in Jiangning and Baguazhou areas were firstly studied. In addition, the spatial variations and structures of heavy metals were also examined using the geostatistics and spatial autocorrelation method. It is the premises for exploring the feasibility of predicting heavy metals in soils using the remote-sensing geochemistry approach to understand the spectral features of soils and heavy metals. Therefore, the reflectance spectra of soils and heavy metals were measured, and then the spectral responses of soils with different pollution levels were researched by means of modeling pollution artificially in the laboratory. Based on these results, the relationships between reflectance spectra and heavy metals in soils of both Jiangning and Baguazhou areas were researched furtherly. The contents of heavy metals of the two suburban areas were successfully predicted using reflectance spectra. Moreover, the mechanism by which to predict the spectrally featureless heavy metals was discussed. Besides reflectance spectra, many other bands can be used by remote sensing technologies. However, due to the atmospheric effects, the electromagnetic waves within the shorter bands, such as X-ray orγ-ray, are not suitable for the earth surface. The feasibility of the spectra within the thermo-infrared region for mapping soil heavy metal pollution was explored. Due to the lack of suitable images of Nanjing area, the potential of remote sensing for monitoring heavy metal pollution in suburban soils of Nanjing area was investigated using the simulated sensors based on the results of the band selection. The results derived from the simulated sensors could provide theoretical base of sensor selection for mapping soil heavy metal pollution rapidly in the future.
The main results of the thesis are listed below:
(1) The new-found problem of Cd anomalies spreading along the whole Changjiang River has been laid store by all the provinces along the River. In this dissertation, two ways, geo-statistics and the sequential extraction were used to research the sources and risk assessment of Cd. Some novel results were achieved from the two ways. Firstly, the results of spatial auto-correlation showed that Cd had low value of spatial autocorrelation, which suggest that the spatial distribution of Cd was scattered and hence implied that the source of Cd may be from the anthropic input. Secondly, the results derived from the sequential extraction showed that Cd had the highest extractability, 82.3%. Considering that the total contents of Cd were far higher than the background value, it could be concluded that in Baguazhou there was high risk for Cd. Moreover, the correlation analysis showed that the residual fractions of Cd were negatively correlated with the total Cd, which further validated the conclusion that besides from the natural materials, Cd in Baguazhou area was also from anthropic input.
(2) In this dissertation, the spectral responses of soil heavy metals were researched detailedly for the first time in the world. For the heavy metals discussed in this dissertation, only Ni, Cr, and Cu were spectral active, while other elements were spectral featureless. Only when the contents of Cr and Cu were as high as 4‰, the heavy metals could be explored from the soil spectra. For the agricultural soils, however, the contents of heavy metals were far lower than this value. Therefore, it was pointed that it could not predict heavy metals in agricultural soils with reflectance spectra only by means of the direct way, and the prediction for heavy metals should be changed a new method.
(3) For the first time in the world, the contents of heavy metals in agricultural soils were predicted with reflectance spectra. The results showed that heavy metals were all negatively correlated with reflectance for both Jiangning and Baguazhou areas. It was very interesting that the order of the prediction accuracy was the same as the order of their correlation coefficients with Fe. It was concluded that the inter-correlation between heavy metals and total Fe in soils was the major mechanism by which to predict spectrally featureless heavy metals.
(4) For the soil samples located in the northwest of Baguazhou and polluted seriously by industries, their spectral curves were significantly different from other spectra of agricultural soils. These high polluted soil samples were identified directly according to their reflectance spectra. The criterion to judge whether heavy metals were from the industrial area or not was: 1) the average reflectance was lower than 30%; 2) the absorption depth around 880nm was more than 2%; and 3) the absorption depth around 2210nm caused by clay minerals was lower than 4%. The method of reflectance spectra was a kind of complementarity for the identification of the source of heavy metals to the traditional methods.
(5) Goethite, which can affect the distribution, transfer and translation of heavy metals in soils, is the major iron oxides in Nanjing area. It’s very difficult to identify goethite using the traditional methods, such as XRD. In this dissertation, goethite in soils was successfully identified using reflectance spectra, and the criterion for the identification was brought forward. The strong d-d absorption band around 490 nm, which corresponds to the electron pair transition (EPT), 2(4T1g), was the diagnostic spectral feature of goethite. Moreover, the absorption depth around 490nm caused by free iron oxides in soils becomes stronger with the increase of free iron oxides. Based on the relationship between the absorption depth and the contents of free iron oxides, free iron oxides in soils can be predicted quickly.
(6) The heavy metal adsorption caused by goethite is the important factor affecting the contents of heavy metals in soils. The results of Ni2+ adsorption isotherm were Langmuir function. Ni2+ adsorption was very low at the low Ni2+ contents. Adsorption became significant when the contents of Ni2+ were higher. The results derived from reflectance spectra, XPS and FT-IR showed that, H+ bonded to Fe-OH was displaced by Ni2+ during the adsorption. During the adsorption, Ni2+ was the base while goethite was the acid. Electrons were transferred from Ni2+ to goethite.
(7) The results of FT-IR showed that, the FT-IR spectra of agricultural soils in Nanjing suburban area only reflected the vibrational bands due to OH and Si-O. The FT-IR spectra were very similar for the soils polluted by different heavy metal levels. Moreover, iron oxides and organic matter, which are related with heavy metals in soils, could not be identified directly from the FT-IR spectra of soils. Therefore, it was concluded that the optimal bands for investigating heavy metal pollution were VNIR band.
(8) Due to the lack of suitable images of the research areas and based on the bands selection derived above, in this dissertation the relationships between the simulated HyMap, TM and Quickbird and heavy metal contents were explored. The prediction accuracy for each sensor was satisfactory and similar. It suggested that low spectral resolution was not a limitation for predicting soil heavy metal contents. Considering the costs and the problem of mixing pixels, it was thought that Quickbird was the first choice for mapping soil heavy metal pollution rapidly.
遥感地球化学能够动态、快速、宏观地获取地表地球化学信息,已广泛应用于环境地球化学和土壤科学等领域。然而,目前利用遥感地球化学方法研究农业土壤重金属污染的研究尚未见报道。这其中最主要的困难在于重金属元素在土壤中含量甚微,现有技术难以直接探测到重金属元素光谱信息。探究土壤重金属元素光谱响应,研究土壤光谱与重金属元素关系是当前遥感地球化学面临的难点之一。
城郊土壤密切接触密集的城市人群,涉及众多生命的健康和安全。严重的重金属污染是城郊农业土壤的一个重要特征。城郊农业土壤污染一方面影响土壤的生态功能,同时污染物通过食物链传递和土壤颗粒物直接吸入而影响城市居民的健康,城郊农业土壤污染已成为公众普遍关注的问题。江宁和八卦洲是南京城南和城北的两处城郊地区,它们是南京重要的粮食和蔬菜供应基地,与南京居民的健康和安全息息相关。
基于以上背景,本文在江苏省地质调查研究院“江苏省国土生态地球化学调查”项目的支持下,选取江宁和八卦洲这两个受人类活动影响较强、且对于工农业生产具有重要位置的地区作为研究区,研究遥感地球化学方法预测农业土壤重金属元素含量可行性,为遥感技术快速制图土壤重金属污染提供理论依据和技术方法途径。本研究一方面可以促进土壤重金属污染研究的时间维和空间维,为土壤重金属污染监测、评价与制图快速、经济地提供信息;另一方面又拓宽了遥感地球化学研究范畴,促进遥感地球化学这门新兴学科的发展。此外,本研究可以为今后研究区的污染控制、风险评估、土地利用规划以及政府决策等提供必要信息,有助于提高粮食安全和维护居民健康,促进区域可持续发展。
遥感地球化学是一门综合性学科,而其基础理论、技术方法和应用又都包罗万象,本论文将研究重点着眼于基础研究。电磁波与地物的相互作用是遥感地球化学探测地表化学成分的基础。土壤光谱特征是遥感地球化学方法快速识别土壤化学成分的关键。利用各种分析技术和方法,努力将土壤光谱和重金属元素结合起来,建立土壤重金属元素与土壤光谱的关系,是本论文最基本目的。
本文以土壤和重金属元素光谱特征为主线,以建立土壤光谱与重金属元素关系为目标,围绕土壤重金属元素的光谱响应进行了系统的探索研究。在研究土壤光谱与重金属元素的关系之前,首先利用传统地球化学方法调研了江宁和八卦洲土壤重金属元素分布特征及它们的化学形态,并基于GIS技术,利用地统计学和空间自相关方法研究了它们的空间变异性。明了重金属元素以及土壤的光谱特征,是探索遥感地球化学方法快速预测土壤重金属元素可行性的前提。为此,本文测试了重金属元素以及南京城郊土壤的反射光谱,进而利用室内模拟污染试验,研究了不同重金属污染级别土壤的反射光谱响应。在此基础上,对江宁和八卦洲土壤反射光谱和重金属元素的关系进行了研究,利用反射光谱对这两个地区土壤重金属元素含量进行了预测,并探讨了反射光谱预测重金属元素的机理。除了反射光谱,遥感地球化学可利用的谱段还有很多,适合于地球遥感的热红外波段在土壤重金属污染研究中能否发挥作用,论文进而对此进行了研究。通过对比反射光谱与热红外光谱的研究结果,从而为遥感地球化学监测土壤重金属污染选择最佳监测波段。基于该结果,在当前难以获取研究区合适遥感影像情况下,通过室内模拟传感器波段反射率预测了南京城郊土壤重金属元素含量,并为今后利用遥感技术快速、大范围制图土壤重金属污染传感器的选择提供了理论依据。论文主要结论和认识如下:
(1)长江流域贯穿全流域的Cd异常是最近几年发现的新问题,目前已引起国土资源部及沿江各省的高度重视。本文将地统计学和化学形态分级提取方法联合用于Cd的来源和污染风险研究,得出了一些较为新颖的结论。首先空间自相关研究表明Cd的空间自相关性很小,说明Cd的空间分布较为零乱,这也暗示Cd的异常可能受到了人为的影响。进而化学形态分析结果表明,Cd以有效态为主,平均占其总量的82.3%。考虑到Cd总量已达轻微污染程度,说明八卦洲地区存在严重的Cd污染风险。此外相关分析表明,残渣态Cd所占比率与总Cd呈负指数关系,这进一步说明八卦洲地区Cd高含量与人类活动有关。
(2)详细研究了土壤重金属元素的反射光谱响应。指出在土壤中,只有具有特征吸收峰的重金属元素,而且当它们的含量相当高时(如Cr和Cu含量为4000 mg/kg)方可表现出自身光谱行为。对于农业土壤来说,很少达到如此高污染。因此本研究指出,直接从光谱吸收特征角度考虑,使用反射光谱快速识别并预测农业土壤重金属元素含量不是可行之策,利用反射光谱预测土壤重金属元素含量必须另辟蹊径。
(3)首次利用反射光谱成功预测了农业土壤重金属元素含量,并对预测机理进行了解释。研究结果表明,无论江宁还是八卦洲,土壤重金属元素与反射率都呈负相关。重金属元素预测精度顺序与它们和铁的相关性顺序一致,即与铁相关性高的元素预测精度也高,与铁相关性低的元素预测精度也低。重金属元素与土壤铁的内部相关是反射光谱预测无光谱特征重金属元素的机理。
(4)除了预测农业土壤重金属元素含量,本文还利用反射光谱识别了受工业严重污染的土壤,并建立了工业污染土壤的光谱识别标准:总体反射率低于30%,880nm附近吸收峰深度大于2%,以及粘土矿物在2210nm附近吸收峰深度小于4%。反射光谱法是对传统土壤重金属污染源识别方法的一种补充。
(5)利用反射光谱成功识别了土壤针铁矿,并提出了识别土壤针铁矿的光谱标识,即针铁矿电子对跃迁2(4T1g)所产生的490nm附近双吸收峰是土壤针铁矿的诊断性光谱特征。通过单一土壤的模拟试验以及对不同土壤的研究,结果表明土壤在490nm波段处吸收峰深度随土壤针铁矿含量增加而加深,二者具有很好的相关关系,可以利用该吸收峰深度快速预测土壤针铁矿含量。
(6)针铁矿对重金属元素的吸附是影响重金属元素在土壤中含量的重要因素。本文进行了针铁矿对重金属元素的吸附研究。结果表明,针铁矿对Ni2+的等温吸附曲线呈“Langmuir”型,在浓度较低时,吸附量随平衡浓度的增加而迅速升高,当平衡浓度达到一定程度时,吸附量也趋于饱和。反射光谱、XPS和FT-IR研究结果表明,针铁矿对Ni2+的吸附为专性吸附,吸附时Ni2+置换针铁矿A型羟基中的H+,Ni2+作为释电子的Lewis碱,针铁矿作为受电子的Lewis酸,电子从Ni2+向针铁矿中的氧和铁转移。
(7)热红外研究结果表明,南京城郊农业土壤的热红外光谱主要反映了粘土矿物OH伸缩振动和Si-O键伸缩和弯曲振动,而与重金属元素有关的有机质和铁氧化物信息则不明显。此外,人为污染模拟试验结果表明,土壤热红外光谱也难以直接显示重金属元素信息。可见,无论是直接还是间接方式,热红外波段都不如可见光近红外(VNIR)波段对重金属元素敏感,因此,本文认为监测土壤重金属污染的最佳波段是VNIR波段。
(8)基于上述最佳波段选择结果,在当前难以获取研究区合适遥感影像情况下,利用实验室模拟传感器波段预测了研究区土壤重金属元素含量。通过对不同平台、不同光谱和空间分辨率传感器HyMap、TM以及Quickbird的模拟,结果表明土壤重金属元素可以被这三种传感器预测,而且它们的预测精度非常接近,说明光谱分辨率不是预测重金属元素含量的关键性因子。综合考虑花费以及混合像元影响,本研究认为Quickbird是快速制图本区农业土壤重金属污染的首选传感器。
Heavy metal pollution has been an important problem in the field of environmental geochemistry all along. Due to the application of modern analytical technologies and computer technology, the analytical accuracies of heavy metals become higher and higher and the analytical limits of detection become lower and lower, which contribute to the research of heavy metal pollution in the area of information dimension. However, the research of soil heavy metal pollution in the area of spatial-temporal dimension still relies on the conventional geochemical methods, which are based on the raster sampling and chemical analysis and hence are time-consuming and relatively expensive. Therefore, it is urgent to find a rapid and cost-effective way for investigating soil heavy metal pollution.
The remote-sensing geochemistry approach, which can allow for synoptic and repetitive coverage of large areas, has been widely used in the area of environmental geochemistry and pedology. To our knowledge, however, until now examples of the use of remote-sensing geochemistry approach investigating heavy metals pollution for agricultural soils have not been reported. The major difficulty is that the contents of heavy metals in soils are very low, and hence it’s very difficult to detect their spectral features with the existing technologies. It is one of the difficulties to explore the spectral responses of heavy metals in soils and research the relationships between the soil spectra and heavy metals.
Soils in suburban environment come easily in contact with humans and hence have an important influence on public health. Serious heavy metal pollution is one of the important properties of many suburban soils. The heavy metal pollution in the suburban soils can affect the ecological functions of soils. On the other hand, the pollutants can influence the townsman’s health by means of food chain or the inhalation of soil particles. Therefore, the problem of heavy metal pollution in suburban agricultural soils has come to attention by the public. Jiangning and Baguazhou areas are located in the south and north of the Nanjing city, respectively. Both areas are the important base for food and vegetable supply of Nanjing, and hence they are closely linked to the people’s health.
Based on the background mentioned above and supported by the project named“biogeochemical survey for the land of Jiangsu province”, which was funded by Geological Survey of Jiangsu Province, the two important areas, Jiangning and Baguazhou, were chosen as the study areas to research the feasibility of the remote sensing approach for predicting heavy metal pollution in agricultural soils. One side, this study can contribute to the research of soil heavy metal pollution in the area of spatial-temporal dimension. On the other hand, this study can widen the research scope of remote-sensing geochemistry, and consequently promote the developments of the new subject, remote-sensing geochemistry. Moreover, this study has the function of enhancing the food security, protecting people’s health, and advancing the sustainable development of Nanjing area.
Remote-sensing geochemistry is a kind of comprehensive subject, the basal theory, technologies and applications of which are extensive. This study mostly focused on the basal research. Soil spectral features are the basis for the identification of soil constituents using the remote-sensing geochemistry approach. The primary objective of this study was to try to find the relationships between soil spectra and soil heavy metals using all kinds of technologies and methods.
In this dissertation, the spectral features of soils and heavy metals were the main line, and building the relationships between soil spectra and the contents of heavy metals was the major aim. Based on these objects, the spectral responses of soil heavy metals were systemicly researched. Before the relationships between soil reflectance spectra and soil heavy metals were explored, the contents and chemical speciations of heavy metals in Jiangning and Baguazhou areas were firstly studied. In addition, the spatial variations and structures of heavy metals were also examined using the geostatistics and spatial autocorrelation method. It is the premises for exploring the feasibility of predicting heavy metals in soils using the remote-sensing geochemistry approach to understand the spectral features of soils and heavy metals. Therefore, the reflectance spectra of soils and heavy metals were measured, and then the spectral responses of soils with different pollution levels were researched by means of modeling pollution artificially in the laboratory. Based on these results, the relationships between reflectance spectra and heavy metals in soils of both Jiangning and Baguazhou areas were researched furtherly. The contents of heavy metals of the two suburban areas were successfully predicted using reflectance spectra. Moreover, the mechanism by which to predict the spectrally featureless heavy metals was discussed. Besides reflectance spectra, many other bands can be used by remote sensing technologies. However, due to the atmospheric effects, the electromagnetic waves within the shorter bands, such as X-ray orγ-ray, are not suitable for the earth surface. The feasibility of the spectra within the thermo-infrared region for mapping soil heavy metal pollution was explored. Due to the lack of suitable images of Nanjing area, the potential of remote sensing for monitoring heavy metal pollution in suburban soils of Nanjing area was investigated using the simulated sensors based on the results of the band selection. The results derived from the simulated sensors could provide theoretical base of sensor selection for mapping soil heavy metal pollution rapidly in the future.
The main results of the thesis are listed below:
(1) The new-found problem of Cd anomalies spreading along the whole Changjiang River has been laid store by all the provinces along the River. In this dissertation, two ways, geo-statistics and the sequential extraction were used to research the sources and risk assessment of Cd. Some novel results were achieved from the two ways. Firstly, the results of spatial auto-correlation showed that Cd had low value of spatial autocorrelation, which suggest that the spatial distribution of Cd was scattered and hence implied that the source of Cd may be from the anthropic input. Secondly, the results derived from the sequential extraction showed that Cd had the highest extractability, 82.3%. Considering that the total contents of Cd were far higher than the background value, it could be concluded that in Baguazhou there was high risk for Cd. Moreover, the correlation analysis showed that the residual fractions of Cd were negatively correlated with the total Cd, which further validated the conclusion that besides from the natural materials, Cd in Baguazhou area was also from anthropic input.
(2) In this dissertation, the spectral responses of soil heavy metals were researched detailedly for the first time in the world. For the heavy metals discussed in this dissertation, only Ni, Cr, and Cu were spectral active, while other elements were spectral featureless. Only when the contents of Cr and Cu were as high as 4‰, the heavy metals could be explored from the soil spectra. For the agricultural soils, however, the contents of heavy metals were far lower than this value. Therefore, it was pointed that it could not predict heavy metals in agricultural soils with reflectance spectra only by means of the direct way, and the prediction for heavy metals should be changed a new method.
(3) For the first time in the world, the contents of heavy metals in agricultural soils were predicted with reflectance spectra. The results showed that heavy metals were all negatively correlated with reflectance for both Jiangning and Baguazhou areas. It was very interesting that the order of the prediction accuracy was the same as the order of their correlation coefficients with Fe. It was concluded that the inter-correlation between heavy metals and total Fe in soils was the major mechanism by which to predict spectrally featureless heavy metals.
(4) For the soil samples located in the northwest of Baguazhou and polluted seriously by industries, their spectral curves were significantly different from other spectra of agricultural soils. These high polluted soil samples were identified directly according to their reflectance spectra. The criterion to judge whether heavy metals were from the industrial area or not was: 1) the average reflectance was lower than 30%; 2) the absorption depth around 880nm was more than 2%; and 3) the absorption depth around 2210nm caused by clay minerals was lower than 4%. The method of reflectance spectra was a kind of complementarity for the identification of the source of heavy metals to the traditional methods.
(5) Goethite, which can affect the distribution, transfer and translation of heavy metals in soils, is the major iron oxides in Nanjing area. It’s very difficult to identify goethite using the traditional methods, such as XRD. In this dissertation, goethite in soils was successfully identified using reflectance spectra, and the criterion for the identification was brought forward. The strong d-d absorption band around 490 nm, which corresponds to the electron pair transition (EPT), 2(4T1g), was the diagnostic spectral feature of goethite. Moreover, the absorption depth around 490nm caused by free iron oxides in soils becomes stronger with the increase of free iron oxides. Based on the relationship between the absorption depth and the contents of free iron oxides, free iron oxides in soils can be predicted quickly.
(6) The heavy metal adsorption caused by goethite is the important factor affecting the contents of heavy metals in soils. The results of Ni2+ adsorption isotherm were Langmuir function. Ni2+ adsorption was very low at the low Ni2+ contents. Adsorption became significant when the contents of Ni2+ were higher. The results derived from reflectance spectra, XPS and FT-IR showed that, H+ bonded to Fe-OH was displaced by Ni2+ during the adsorption. During the adsorption, Ni2+ was the base while goethite was the acid. Electrons were transferred from Ni2+ to goethite.
(7) The results of FT-IR showed that, the FT-IR spectra of agricultural soils in Nanjing suburban area only reflected the vibrational bands due to OH and Si-O. The FT-IR spectra were very similar for the soils polluted by different heavy metal levels. Moreover, iron oxides and organic matter, which are related with heavy metals in soils, could not be identified directly from the FT-IR spectra of soils. Therefore, it was concluded that the optimal bands for investigating heavy metal pollution were VNIR band.
(8) Due to the lack of suitable images of the research areas and based on the bands selection derived above, in this dissertation the relationships between the simulated HyMap, TM and Quickbird and heavy metal contents were explored. The prediction accuracy for each sensor was satisfactory and similar. It suggested that low spectral resolution was not a limitation for predicting soil heavy metal contents. Considering the costs and the problem of mixing pixels, it was thought that Quickbird was the first choice for mapping soil heavy metal pollution rapidly.
引文
1. Adams, J.W.; Bennett, H.H. “Using reflectance Fourier transform infrared spectroscopy (FT-IR) for rapid analysis of contaminated soils,” Contam. Soils, 1996, 1:241-252.
2. Alloway, B.J. Soil processes and behavior of metals. In: Alloway, B.J (ed.) Heavy metals in soils. New York, 1990.
3. Balsam, W.L.; Deaton, B.C. Sediment dispersal in the Atlantic Ocean: Evaluation by visible light spectra. Rev. Aquatic Sci, 1991, 4:411- 447.
4. Balsam, W.L.; Wolhart, R. Sediment dispersal in the Argentine Basin: evidence from visible light spectra. Deep-Sea Res, 1993, Part A, 40:1001-1031.
5. Baumgardner, M.F.; Kristof, S.; Johannsen, C.J., et al. Effects of organic matter on the multispectral properties of soils. Proc. Indiana Acad. Sci., 1970, 79:413 - 422.
6. Baugh, W.M.; Kruse, F.A.; Atkinson, W.W. Quantitative Geochemical Mapping of Ammonium Minerals in the Southern Cedar Mountains, Nevada, Using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ, 1998, 65: 292-308.
7. Bedidi, A.; Cervelle, B.; Madeira, J., et al. Moisture effects on visible spectral characteristics of lateritic soils. Soil Sci, 1992, 153:129-141.
8. Ben-Dor, E.; Banin, A. Visible and near infrared (0.4-1.1mm) analysis of arid and semi arid soils. Remote Sensing of Environment, 1994, 48:261-274.
9. Ben-Dor, E.; Banin, A. Near infrared analysis (NIRA) as a rapid method to simultaneously evaluate, several soil properties. Soil Science Society of American Journal, 1995, 59:364-372.
10. Ben-Dor, E. Quantitative Remote Sensing of Soil Properties. Advances in Agronomy, 2000, 75:173-243.
11. Bertsch, P.M.; Seaman, J.C. Characterization of complex mineral assemblages: implications for contaminant transport and environmental remediation. Proc Natl Acad Sci U S A, 1999, March 30, 96(7):3350-3357.
12. Bowers, S.S.; Hanks, R.J. Reflectance of radiant energy from soils. Soil Science, 1965, 100:130-138.
13. Brewer, M.; Scott, T. Concise Encyclopedia of Biochemistry. Walter de Gruyter, Berlin, New York, 1983.
14. Burns, R.G. Mineralogical Applications of Crystal Field Theory. Cambridge University Press, Cambridge, 1970.
15. Burns, R.G. Thermodynamic data from crystal field spectra. in Microscopic to Macroscopic. Atomic Environments to Mineral Thermodynamics (S.W. Kieffer and A. Navrotsky, eds.). Mineralogical Society of America. Published in Rev. Mineral., 1985a, 14:277-316.
16. Burns, R.G. Mineralogical Applications of Crystal Field Theory, Second Edition, Cambridge University Press, Cambridge, 1993.
17. Chabrillat, S.; Goetz, A.F.H.; Krosley, L., et al. Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution. Remote Sensing of Environment, 2002, 82:431-445.
18. Chapman, J.E.; Rothery, D.A.; Francis, P.W. Remote sensing of evaporite mineral zonation insalt flats (salars). Int. J. Remote Sens., 1989, 10:245-255.
19. Chlopecka, A.; Bacon, J.R.; Wilson, M.J., et al. Forms of cadmium, lead, and zinc in contaminated soils from southwest Poland. J. Environ. Qual, 1996, 25:69-79.
20. Christensen, P.R.; Bandfield, J.L.; Hamilton, V.E., et al. A thermal emission spectral library of rock-forming minerals. J. Geophys. Res., 2000, 105:9735-9739.
21. Chung, h.; Lee, J.S.; Ku, M.S. Feasibility of simultaneous measurement of Xylene Isomers and other hydrocarbons in p-Xylene production processes using near-infrared spectroscopy. Appl. Spectrosc. 1998, 52(6):885-889.
22. CIDA (Canada International Development Agency). An urbanized world, statement on sustainable cities. Ministry of Public Works and Government Services Canada, Canada. 1998.
23. Clark, I. Practical Geostatistics. London Applied Science Publishers, 1979.
24. Clark, R.N.; Roush, T.L. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res., 1984, 89:6329-6340.
25. Clark, R.N.; King, T.V.V.; Klejwa, M., et al. High Spectral Resolution Reflectance Spectroscopy of Minerals. J. Geophys Res., 1990a, 95:12653-12680.
26. Clark, R.N. Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In Manual of Remote Sensing, 1999.
27. Clark, R.N.; Swayze, G.A.; Livo, K.E., et.al. Imaging Spectroscopy: Earth and Planetary Remote Sensing with the USGS Tetracorder and Expert Systems. Journal of Geophysical Research-planets, 2003,108 (E12):Art. No. 5131 DEC 6.
28. Cloutis, E.A. Hyperspectral Geological Remote Sensing: Evaluation of Analytical Techniques. Int. J. Remote Sensing, 1996,17 (12):2215-2242.
29. Condit, H.R. The spectral reflectance of American soils. Photogrammetric Engineering, 1970, 36:955-966.
30. Corwin, D.L.; Wagenet, R.J. Applications of GIS to the modeling of non-point source pollutants in the vadose zone: A conference overview. Journal of Environmental Quality, 1996, 25(3):403-411.
31. Corwin, D.L.; Loague, K.; Ellsworth, T.R. GIS-based modeling of nonpoint source pollutants in the vadose zone. Journal of Soil and Water Conservation, 1998, 53 (1):34-38.
32. Crowley, J.K. Visible and near-infrared (0.4-2.5 mm) reflectance spectra of playa evaporite minerals. Journal of Geophysical Research, 1991, 96 (16):231-240.
33. Davies, B.E. Consequences of environmental contamination by lead mining in Wales. Hydrobiologia, 1987, 49:213.
34. Davis, A.P.; Upadhyaya, M. Desorption of camium from goethite. Wat. Res, 1996, 30(8):1894-1904.
35. Dehaan, R.L.; Taylor, G.R. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization. Remote Sens. Envir., 2002, 80:406- 417.
36. Dematte, J.A.M.; Camposa, R.C.; Alves, M.C., et al. Visible–NIR reflectance: a new approach on soil evaluation. Geoderma, 2004, 121:95-112.
37. Drake, N.A. Reflectance spectra of evaporite minerals (400-2500 nm): applications for remote sensing. Int. J. Remote Sens, 1995, 16:2555-2571.
38. Duke, E.F. Near Infrared Spectra of Muscovite, Tschermak Substitution, and Metamorphic Reaction Progress: Implications for Remote Sensing. Geology, 1994, 22:621-624.
39. Farmer, V.C. The Infra-Red Spectra of Minerals, (V.C. Farmer, ed.) Mineralogical Society,London, 1974.
40. Fidencio, P.H.; Poppi, R.J.; Andrade, J.C. Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy. Analytica Chimica Acta, 2002, 453:125-134.
41. Frangi, J.P.; Richard, D. Heavy metal soil pollution cartography in northern France. The Science of the Total Environment, 1997, 205:71-79.
42. Frazier, B.E.; Cheng, Y. Remote sensing of soils in the Eastern Palouse region with Landsat Thematic Mapper. Remote Sens. Envir., 1989, 28:317-325.
43. Gaffey, S.J. Spectral Reflectance of Carbonates Minerals in the Visible and Near Infrared (0.35-2.55 microns): Calcite, Aragonite, and Dolomite. American Mineralogist, 1986, 71:151-162.
44. Galvao, L.S.; Vitorello, I. Role of organic matter in obliterating the effects of iron on spectral reflectance and colour of Brazilian tropical soils. Int. J. Remote Sens, 1998, 19:1969-1979.
45. Galvao, L.S. Variations in Reflectance of Tropical Soils: Spectral-Chemical Composition Relationships from AVIRIS data. Remote sens. Envir., 2001, 75:245-255.
46. Gao, B.C.; Goetz, A.F.H.; Wiscombe, W.J. Cirrus cloud detection from airborne imaging spectrometer data using the 1.38mm water vapor band. Geophysical research letter, 1993b, 40(4):301-304.
47. Garcia-Sanchez, A.; Moyano, A.; Munez, C. Forms of cadmium, lead, and zinc in polluted mining soils and uptake by plants (Soria province, Spain). Commun.Soil Sci. Plant Anal, 1999, 30:1385-1402.
48. Gong, P.; Pu, R.; Heald, R.C. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. International Journal of Remote Sensing, 2002, 23 (9):1827-1850.
49. Grant, R.; Grant, C. Grant and Hackh's Chemical Dictionary. McGraw-Hill, New York, 1987.
50. Grossl, P.R.; Sparks, D.L. Evaluation of contaminant ion adsorption/desorption on goethite using pressure-jump relaxation kinetics. Geoderma, 1995, 67:87-101.
51. Hammaker, R.M.; Poholarz, J.M.; Marshall, T.L., et al. “Remote sensing of volatile organic compounds in the atmosphere using a Fourier transform near-infrared spectrometer,” Leaping Ahead Near-infrared Spectrosc., (Proc. Int. Conf. Near-infrared Spectrosc.), 6th EDITOR: Batten, G. D. (Ed.), 1995:510-513.
52. Hapke, B. Bidirectional Reflectance Spectroscopy, 1, Theory. Journal of Geophysical Research, 1981, 86:3039-3054.
53. Hapke, B. Theory of Reflectance and Emittance Spectroscopy. Cambridge University Press, 2005.
54. Hauff, P.L.; Peters, D.C.; Borstad, G.W., et al. Hyperspectral investigations of mine waste and abandoned mine lands - The Dragon calibration site case study. Summaries of the Ninth Annual JPL Airborne Earth Science Workshop, JPL, Pasadena, February, 2000:23-25.
55. Henderson, T.L.; Szilagyi, A.; Baumgardner, M.F., et al. Spectral band selection for classification of soil organic matter content. Soil Sci.Soc.Am. J., 1989, 53:1778-1784.
56. Hesham, A.S.; Watson, P.R. An XPS study of the adsorption of lead on goethite a-FeOOH. Applied Surface Science, 1998, 136:46-54.
57. Hoogenboom, H.J.; Dekker, A.G.; Althuis, I.J.A. Simulation of AVIRIS Sensitivity for Detecting Chlorophyll over Coastal and Inland Waters. Remote Sensing of Environment, 1998, 65:333-340.
58. Hunt, G.R.; Salisbury, J.W. Visible and near infrared spectra of minerals and rocks. I. Silicate minerals, Mod. Geology, 1970, 1:283-300.
59. Hunt, G.R.; Salisbury, J.W. Visible and near infrared spectra of minerals and rocks. II. Carbonates, Mod. Geology, 1971, 2:23-30.
60. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and nearinfrared spectra of minerals and rocks: IV. Sulphides and sulphates. Mod. Geol., 1971, 3:1-14.
61. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and near infrared spectra of minerals and rocks. III. Oxides and hydroxides, Mod. Geology, 1971a, 2:195-205.
62. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and near infrared spectra of minerals and rocks. IV. Sulphides and sulphates, Mod. Geology, 1971b, 3: 1-14.
63. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and near infrared spectra of minerals and rocks. V. Halides, arsenates, vanadates, and borates, Mod. Geology, 1972, 3:121-132.
64. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and near infrared spectra of minerals and rocks. VI. Additional silicates, Mod. Geology, 1973, 4:85-106.
65. Idso, S.B.; Jackson, R.D.; Reginato, R.J., et al. The dependence of bare soil albedo on soil water content. Journal of Applied Meteorology, 1975, 14:109-113.
66. Irons, J.R.; Weismiller, R.A.; Petersen, G.W. Soil reflectance. In: Theory and application of optical remote sensing (Asrar G. ed.). New York: John Wiley and Sons, 1989:66-106.
67. Islam, K.; Singh, B.; McBratney, A.B. Simultaneous estimation of several soil properties by ultra-violet, visible and near infrared reflectance spectroscopy. Australian Journal of Soil Research, 2003, 41:1-14.
68. Ji, J.F.; Balsam, W.L.; Chen, J., et al. Rapid and quantitative measurement of hematite and goethite in the Chinese Loess-Paleosol sequence by diffuse reflectance spectroscopy. Clays and Clay Miners, 2002, 50:210-218.
69. Ji, J.F.; Chen, J.; Jin, L., et al. Relating magnetic susceptibility (MS) to the simulated thematic mapper (TM) bands of the Chinese loess: Application of TM image for soil MS mapping on Loess Plateau. J. Geophys. Res., 2004, 109 (B5):Art. No. B05102.
70. Johnson, B.B. Effects of pH, temperature and concentration on the adsorption of cadmium on goethite Environ. Sci. Technol., 1990, 24(1):112-116.
71. Johnson, P.E.; Smith, M.O.; Adams, J.B. Simple Algorithms for Remote Determination of Mineral Abundances and Particles Sizes from Reflectance Spectra. Journal of Geophysical Research, 1992, 97 (E2):2649-2657.
72. Kaiser, J. Toxicologists shed new light on old poisons. Science, 1998, 279(5358):1850-1851.
73. Karnieli, A.; Tsoar, H. Spectral reflectance of biogenic crust developed on desert dune sand along the Israel-Egypt border. Int. J. Remote Sens., 1995, 16:369-374.
74. Karnieli, A.; Sarafis, V. Reflectance spectrophotometry of cyanobacteria within soil crusts-a diagnostic tool. Int. J. Remote Sens., 1996, 17 (8):1609-1614.
75. Karnieli, A. Development and implementation of spectral crust index over dune sands. Int.J. Remote Sens., 1997, 18:1207-1220.
76. Kemper, T.; Sommer, S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environmental science and technology, 2002, 36:2742-2747.
77. Khawlie, M.; Awad, M.; Shaban, A., et al. Remote sensing for environmental protection of the eastern Mediterranean rugged mountainous areas, Lebano. ISPRS Journal ofPhotogrammetry & Remote Sensing, 2002, 57:13-23.
78. Kimbrough, D.E; Cohen, Y.; Winer, A.M., et al. A critical assessment of chromium in the Environment. Critical Reviews in Environmental Science and Technology, 1999, 29(1):1-46.
79. Knapp, E.P.; Herman, J.S.; Hornberger, G.M., et al. The effect of distribution of iron-oxyhydroxide grain coatings on the transport of bacterial cells in porous media. Environ. Geol., 1998, 33:243-248.
80. Kooistra, L.; Wehrens, R.; Leuven, R.S.E.W., et al. Possibilities of visible near infrared spectroscopy for the assessment of soil contamination in river floodplains. Analytica Chimica Acta, 2001, 446:97-105.
81. Kubelka, P.; Munk, F. Ein Beitrag zur Optik der Farbanstriche. Z. Tech. Phys., 1931, 11:593-603.
82. Lee, T.C.; Tanaka, N.; Lamb, P.W., et al. Induction of gene amplification by arsenic. Science, 1998, 241:79-81.
83. Leeman, V. The NASA Earth resources spectral information system. A data compiletion. Michgan University, 1971.
84. Lévesque, J.; Neville, R.; Staenz, K. Truong, Q. Preliminary Results on the Investigation of Hyperspectral Remote Sensing for the Identification of Uranium Mine Tailings; Proceedings of ISSSR'01, Québec City, Canada, June, 2001:10-15.
85. Levine, E.R.; Kimes, D.S. Predicting soil carbon in Mollisols using neural networks, In: Soil Processes and the Carbon Cycle. Edited by Rattan Lal, CRC Press, 1998:473-484.
86. Li, X.D.; Coles, B.J.; Ramsey, M.H., et al. Sequential extraction of soils for multielement analysis by ICP-AES. Chemical Geology, 1995, 124:109-123.
87. Li, X.D.; Poon, C.S.; Liu, P.S. Heavy metal contamination of urban soils and street dusts in Hong Kong. Applied Geochemistry, 2001, 16:1361-1368.
88. Liu, W.D.; Baret, F.; Gu, X..F., et al. Evaluation of methods for soil surface moisture estimation from reflectance data. Int. J. Remote Sens., 2003, 24 (10):2069-2083.
89. Madeira, J.; Bedidi, A.; Cervelle, B., et al. Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: the application of a Thematic Mapper (TM) image for soil mapping in Brasilia, Brazil. Int. J. Remote Sens., 1997, 18:2835-2852.
90. Madrid, L.; Diaz-Barrientos, E.; Madrid, F. Distribution of heavy metal contents of urban soils in parks of Seville. Chemosphere, 2002, 49:1301-1308.
91. Malley, D.F.; Williams, P.C. Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environ. Sci. Technol., 1997, 31:3461-3467.
92. Martens, H. Reliable and relevant modeling of real world data: a personal account of the development of PLS Regression. Chemometrics and Intelligent Laboratory Systems, 2001. 58:85–95.
93. Meera, F.V.D. Analysis of spectral absorption features in hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 2004, 5:55-68.
94. Miller, J.R.; Lechler, P.J.; Desilets, M. The role of geomorphic processes in the transport and fate of mercury in the Carson Rover basin, west-central Nevada. Environmental geology, 1998, 33(4):249-262.
95. MINDEC. Ministerial Declaration of the Fourth International Conference on the Protection of the North Sea. Esjberg, Denmark, June 1995:8-9.
96. Montgomery, O.L. An Investigation of the Relationship between Spectral Reflectance and the Chemical, Physical, and Genetic Characteristics of Soils. Ph.D. thesis, Purdue University, West Lafayette, Indiana, 1976.
97. Moran, P.A.P. Notes on continuous stochastic phenomena. Biometrika, 1950, 37:17-23.
98. Mossop, K.F.; Davidson, C.M. Comparison of original and modified BCR sequential extraction procedures for the fractionation of copper, iron, lead, manganese and zinc in soils and sediments. Analytica Chimica Acta, 2003, 478:111-118.
99. Moulder, J.F.; Stickle, W.F.; Sobol, P.E., et al. Handbook of Photoelection Spectroscopy, Perkin-Elmer. Co. Physical Electronics Divisions USA, 1992.
100. Muller, G. Index of geoaccumulation in sediments of the Rhine River. Geojournal, 1969, 2(3):108-118.
101. Muller, E.; Decamps, H. Modeling soil moisture reflectance. Remote Sens. Envir., 2000, 76:173-180.
102. Murray, I. Aspects of interpretation of NIR spectra, in Analytical Application of Spectroscopy (C.S. Creaser and A.M.C. Davies, Eds.), Royal Society of Chemistry, London, 1988:9-21.
103. Mustard, J.F.; Pieters, C.M. Photometric Phase Functions of Common Geologic Minerals and Applications to Quantitative Analysis of Mineral Mixture Reflectance Spectra. Journal of Geophysical Research, 1989, 94 (B10):13619-13634.
104. Mustard, J.F.; Sunshine, J.M. Mineral chemistry from reflectance application of the MGM to minerals of the tremolite-ferroactinolite solid solution series. EOS, 1992, 73:187.
105. Naidu, R.; Sumner, M.E.; Harter, R. Sorption of heavy metals in strongly weathered soils: an overview. Environmental Geochemistry and Health, 1998, 20:5-9.
106. Nickson, R.; Mcarthur, J.; Burgess, W., et al. Arsenic poisoning of Bangladesh groundwater. Nature, 1998, 395:338.
107. Norris, K.H.; Williams, P.C. Optimization of mathematical treatment of raw near-infrared signal in the measurements of protein in hard red spring wheat. I: Influence of particle size, Cer. Chem., 1984, 61:158-165.
108. Obukhov, A.I.; Orlov, D.C. Spectral reflectivity of the major soil groups and possibility of using diffuse reflection in soil investigations. Pochvovedeniye, No.2, 1964.
109. Oliveira, S.M.B.D. Soils as an important sink for mercury in the Amazon. Water, Air Soil Pollut., 2001, 26:321-337.
110. Peacock, C.L.; Sherman, D.M. copper sorption onto goethite, hematite and lepidocrocite: a surface complexation model based on ab initio molecular geometries and EXAFS spectroscopy. Geochimica et Cosmochimica Acta, 2004, 68(12):2623-2637.
111. Pieters, C.M.; Englert, P.A. Remote Geochemical Analysis: Elemental and Mineralogical Composition. Cambridge University Press, 1993.
112. Pinker, R.T.; Karnieli, A. Characteristic spectral reflectance of semi-arid environment. Int. J. Remote Sens., 1995, 16:1341-1363.
113. Pueyo, M.; Sastre, J.; Hernández, E., et al. Prediction of Trace Element Mobility in Contaminated Soils by Sequential Extraction. J. Environ. Qual., 2003, 32:2054-2066.
114. Querauviller, P.H.; Rauret, G.; Griepiuk, B. Single and sequential extraction in sediments and soils. International Journal of Environmental Analytical Chemistry, 1993, 51:231-240.
115. Ramos, L.; Hermamdez, L.M.; Gonzalez, J. Sequential fractionation of Copper, Lead, Cadmium and Zinc in soils from or near Donana Nationl Park. J. Environ. Qual., 1994,23:50-57.
116. Rauret, G.; Lopez-Sanchez, J.F.; Sahuquillo, A., et al. Improvement of the BCR three step sequential extraction procedure prior to the certification of new sediment and soil reference materials. Journal of Environmental Monitoring, 1999, 1(1):57-61.
117. Reeves III, J.B.; McCarty, G.W.; Mimmo, T. The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environmental Pollution, 2002, 116,S277-S284.
118. Sadtler. Minerals: Infrared grating spectra. Sadtler research laboratories, Pennsylvania, 1973.
119. Sahuquilloa, A.; Lopez-Sanchez, J.F.; Rubioa, R., et al. Use of a certified reference material for extractable trace metals to assess sources of uncertainty in the BCR three-stage sequential extraction procedure. Analytica Chimica Acta, 1999, 382:317-327.
120. Scheinost, A.C.; Ford, R.G.; Sparks, D.L. The role of Al in the formation of secondary Ni precipitates on pyrophyllite, gibbsite, talc, and amorphous silica: A DRS study. Geochimica et Cosmochimica Acta, 1999, 63:3193-3203.
121. Schwertmann, U. Some properties of soil and synthetic iron oxides. p. 203–250. In J.W. Stucki et al. (ed.) Iron in soils and clay minerals. D. Reidel, Dordrecht, the Netherlands, 1988
122. Schwertmann, U.; Cornell, R.M. Iron Oxides in the Laboratory; Preparation and Characterization. Weinheim, New York, 1991.
123. Scott, J.S.; Smith, P.G. Dictionary of Waste and Water Treatment. Butterworths, London, 1981.
124. Shang, J.; Lévesque, J.; Howarth, P., et al. Preliminary Investigation of Acid Mine Drainage Detection Using casi Data, Copper Cliff, Ontario, Canada; Fourth International Airborne Remote Sensing Conference and Exhibition/21st Canadian Symposium on Remote Sensing, Ottawa, Ontario, Canada, 21-24 June, 1999:771-778.
125. Shenk, J.S.; Westerhaus, M.O. Analysis of Agriculture and food Products by Near-Infrared Reflectance Spectroscopy; Monograph; Infrasoft International: Port Matilda, PA, 1993:103.
126. Sherman, D.M.; Waite, T.D. Electronic spectra of Fe3+ oxides and oxide hydroxides in the near IR to near UV. Amer. Mineral, 1985, 70:1262-1269.
127. Soils Survey Staff, Bureau of Plant Industry: “Soil Survey Manual”. Handbook No. 18, U. S. Department of Agriculture, Washington, D.C., 1951.
128. Sparks, D. L. Environmental Soil Chemistry. Academic Press, San Diego, 1995.
129. Stoner, E.R.; Baumgardner, M.F. Characteristic variations in reflectance on surface soils, Soil Sci. Soc. Am. J., 1981, 45:1161-1165.
130. Sunshine, J.M.; Pieters, C.M.; Pratt, S.F. Deconvolution of mineral absorption bands: an improved approach. Journal of Geophysical Research, 1990, 95 (B5):6955-6966.
131. Sunshine, J.M.; Pieters, C.M. Determining the Composition of Olivine from Reflectance Spectroscopy. Journal of Geophysical Research, 1998, 103 (E6):13675-13688.
132. Swayze, G.A.; Clark, R.N.; Pearson, R.M., et al. Mapping acid-generating minerals at the California Gulch Superfund Site in Leadville, Colorado using imaging spectroscopy: Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, JPL Publication, 1996:96-4, vol. 1, March 4-8, 231-234.
133. Swayze, G.A.; Clark, R.N.; Hageman, P.L., et al. Using imaging spectroscopy to cost-effectively locate acid-generating minerals at mine sites: an example from the California Gulch Superfund site at Leadville, Colorado. Summaries of the Seventh Annual JPL AirborneEarth Science Workshop, JPL, Pasadena, 12-I 6 January, 1998.
134. Swayze, G.A.; Smith, K.S.; Clark, R.N., et al. Using Imaging Spectroscopy to Map Acidic Mine Waste", Environmental Science and Technology, 2000, 34:47-54.
135. SWES 453/553. Soil Optical Properties and Monitoring Soil Processes. http://tbrs.arizona.edu/education/553/index.htm.
136. Tessier, A.; Campbell, P.G.C.; Bisson, M. Sequential extraction procedure for the speciation of particulate trace metals. Anal. Chem., 1979, 51:844-851.
137. Uhlenbrock, S.; Scharfschwerdt, C.; Neumann, M., et al. The influence of defects on the Ni 2p and O1s xps of NiO. J. Phys. Condens. Matter, 1992:7973-7978.
138. Ure, A.M.; Quevauviller, P.; Muntau, H., et al. Speciation of heavy metals in soils and sediments——an account of the improvement and harmonization of extraction techniques undertaken under the auspices of the BCR of the commission-of-the-European-Communities. International Journal of Environmental Analytical Chemistry, 1993, 51 (1-4):135-151.
139. Ure, A.M.; Davison, C.M.E. Chemical Speciation in the Environment. Blackie Academic and Professional, London, 1995.
140. USPHS. Toxicological profile on CD-ROM. Agency for Toxic Substances and Disease Registry, 1997.
141. USPHS. 8TH Report on Carcinogens 1998 Summary, 1998.
142. Van, D.B.W.; Wienke, D.; Melssen, W., et al. Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS). Chemometrics and Intelligent Laboratory Systems, 1996, 35:187-197.
143. Watson, S.M.; Kroutil, R.T.; Traynor, C.A., et al. “Remote-sensing and in situ atmospheric chemistry studies with the use of a manned hot-air balloon platform,” Field Anal. Chem., Technol., (Field Analytical Chemistry and Technology), 1996, 1(1):13-22.
144. Wilcox, C.H.; Frazier, B.E.; Ball, S.T. Relationship between soil organic carbon and Landsat-Tm data in Eastern Washington. Photogrammetric Engineering and Remoter Sensing. 1994, 60 (6):777-781.
145. Wold, S.; Martens, H.; Wold, H. The multivariate calibration method in chemistry solved by the PLS method. In: Ruhe, A. and Kagstrom, B. (Eds.), Proc. Conf. Matrix Pencils, Lecture Notes in Mathematics, Springer-Verlag, Heidelberg, 1983:286-293.
146. Wong, S.C.; Li, X.D.; Zhang, G., et al. Heavy metals in agricultural soils of the Peral River Delta, South China. Environmental Pollution, 2002, 119:33-44.
147. Xu, F.L.; Zhou, D.H.; Cheng, X.Q., et al. Influence of phosphate on Cu ion adsorption desorption by goethite. 17th WCSS, Thailand, August 2002:14-21.
148. Yu, K.C.; Tsai, L.J.; Chen, S.H., et al. Correlation analyses on binding behavior of heavy metals with sediment matrices. Wat. Res., 2001, 35(10):2417-2428.
149. Zhang, C.S.; Olle, S. Statistics and GIS in environmental geochemistry-some problems and solutions. Journal of Geochemical Exploration, 1998, 64:339-353.
150. 陈怀满, 等. 土壤-植物系统中的重金属污染. 北京: 科学出版社, 1996, 7-9.
151. 陈晋阳, 黄卫. 无定形氢氧化铁吸附水溶液中镉离子机理的 XPS 研究. 分析测试学报, 2002, 21(3):70-72.
152. 成杭新, 杨忠芳, 奚小环, 等. 长江流域沿江镉异常示踪与追源的战略与战术. 第四纪研究, 2005, 25(3):285-290.
153. 戴昌达. 中国主要土壤光谱反射特性分类与数据处理的初步研究. 见:遥感文选. 北京:科学出版社, 1981.
154. 戴前进. 土壤汞的地球化学行为及其污染的防治对策. 地质地球化学, 2002, 30(4): 75-79.
155. 地质情报研究所. 遥感专辑, 第一辑. 北京: 地质出版社, 1980.
156. 丁爱芳, 潘根兴. 南京城郊零散菜地土壤与蔬菜重金属含量及健康风险分析. 生态环境, 2003, 12(4):409-411.
157. 方海兰, 徐凤琳, 刘凡, 等. 中南地区几种地带性土壤中氧化铁类型与钼吸附特性. 热带亚热带土壤科学, 1997, 6(3):182-186.
158. 高小杰. 城郊土壤-蔬菜系统污染评价研究. 江苏环境科技, 1997, 2:16-20.
159. 甘甫平, 刘圣伟, 周强. 德兴铜矿矿山污染高光谱遥感直接识别研究. 地球科学, 2004,29 (1):119-126.
160. 郭进义, 杨忠芳. 浅议地球化学的外延. 地球科学进展, 1998, 13 (1):78-80.
161. 何挺. 土地质量高光谱遥感监测方法研究. 博士论文, 2003.
162. 何延波, 杨琨, 侯英雨. 浅谈遥感地球化学. 地质地球化学, 1997, 4:98-103.
163. 侯景儒, 黄竞先. 地质统计学的理论与方法. 北京: 地质出版社, 1990.
164. 江苏省农业厅. 江苏省农业环境质量报告书. 南京: 江苏省农业厅出版, 1990.
165. 李德仁. 论 21 世纪遥感与 GIS 的发展. 武汉大学学报(信息科学版), 2003, 28(2):127-131.
166. 李克斌, 刘维屏, 邵颖. 重金属离子在腐殖酸上吸附的研究. 环境污染与防治, 1997,
19(1):9-11.
167. 廖启林, 吴新民, 翁志华, 等. 南京地区多目标地球化学调查基本成果及其相关问题初探. 中国地质, 2004, 31(1):70-77.
168. 刘凡, 徐凤琳, 李学垣, 等. 几种可变电荷土壤中氧化铁类型与磷吸附特性. 华中农业大学学报, 1993, 12(5):450-454.
169. 刘芬, 陈萦. 含铁化合物的 Fe 2p 和 Fe3s 电子能谱研究. 分析测试技术与仪器, 2001, 7(3):166-169.
170. 刘伟东. 高光谱遥感信息提取与挖掘研究. 博士论文, 2002.
171. 刘玉洁, 杨忠东, 等. MODIS 遥感信息处理原理与算法. 北京: 科学出版社, 2001:2-12.
172. 陆常青. 土壤粘粒的红外光谱. 土壤, 1986, 18 (2):94-100.
173. 卢瑛, 龚子同, 张甘霖. 南京城市土壤中重金属的化学形态分布. 环境化学, 2003, 22(2):131-136.
174. 马毅杰. 长江中下游土壤矿物组成与其土壤肥力. 长江流域资源与环境, 1994, 3(1):1-8.
175. 彭文世, 刘高魁. 矿物红外光谱图集. 科学出版社, 1982.
176. 浦瑞良, 宫鹏. 高光谱遥感及其应用. 北京: 高等教育出版社, 2000:193-202.
177. 任若恩, 王惠文. 多元统计数据分析. 北京: 国防工业出版社, 1997.
178. 斯韦恩, P. H.; 戴维主, S. M. 遥感定量方法. 北京: 科学出版社, 1984, 153-160.
179. 孙波, 周生路, 赵其国. 基于空间变异分析的土壤重金属复合污染研究. 农业环境科学学报, 2003, 22(2):248-251.
180. 唐诵六. 环境中若干元素的自然背景值及研究方法: 南京地区土壤重金属浓度的概率分布. 中国科学院环境背景值学术交流会论文文集, 1982:9-15.
181. 田国良. 土壤水分的遥感监测方法. 环境遥感, 1991, 6(2):89-98.
182. 王海, 王春霞, 王子健. 太湖表层沉积物中重金属的形态分析. 环境化学, 2002, 21(5):430-435.
183. 王浩. 南京地区城市土壤背景值研究及环境质量分析. 硕士论文, 2003.
184. 王润生, 等. 成像光谱方法技术开发应用研究. 国土资源部“九五”重点科研项目报告, 航空物探遥感中心, 1999.
185. 王绍庆. 土壤和水体反射光谱特性及其应用. 见:童庆禧等主编.中国典型地物波谱及其特征分析. 北京: 科学出版社, 1990:611-618.
186. 王学军, 李本纲, 陶澍. 土壤微量金属含量的空间分析. 北京: 科学出版社, 2005.
187. 王玉, 张一平, 陈思根. 中国 6 种地带性土壤红外光谱特征研究. 西北农林科技大学学报, 2003, 31 (1):57-61.
188. 吴新民, 李恋卿, 潘根兴, 等. 南京市不同功能城区土壤中重金属 Cu、Zn、Pb 和 Cd 的污染特征. 环境科学, 2003, 24(3):105-111.
189. 吴昀昭, 田庆久, 季峻峰, 等. 遥感地球化学研究. 地球科学进展, 2003, 18 (2):228-235.
190. 夏增禄. 中国土壤环境容量. 北京: 地震出版社, 1992.
191. 邢光熹, 朱建国. 土壤微量元素和稀土元素化学. 北京: 科学出版社, 2003.
192. 徐彬彬, 等. 土壤光谱反射特性与理化性状的相关分析. 土壤专报, 第 41 号, 科学出版社, 1986.
193. 徐彬彬, 季耿善. 宁芜土壤遥感研究的主要成果. 土壤专报, 1987, 41 号:5-8.
194. 徐彬彬等. 中国陆地背景和土壤光谱反射特性的地理分区的初步研究. 环境遥感, 1991.
195. 徐冠华. 论热红外遥感中的基础研究. 中国科学(E), 2000, 30, 1-5.
196. 徐瑞松. 遥感生物地球化学. 广东科技出版社, 2003.
197. 杨学义. 环境中若干元素的自然背景值及研究方法: 南京地区土壤背景值与母质的关系. 中国科学院出版(中科院环境背景学术交流会论文文集), 1982:16-20.
198. 杨忠芳, 奚小环, 成杭新, 等. 区域生态地球化学评价核心与对策. 第四纪研究, 2005, 25(3):275-283.
199. 俞天智, 滕秀兰, 张子瑜, 等. 用荧光光谱研究腐殖酸与金属离子 Al 的配合作用. 环境化学, 1999, 18(6):557-560.
200. 赵其国. 为跨世纪土壤学的发展作出新贡献——第 15 届国际土壤学会会议综述. 土壤学报, 1995, 32(1):1-13.
201. 赵其国. 土壤与环境问题国际研究概况及其发展趋向——参加第 16 届国际土壤学会专题综述. 土壤, 1998, 6:281-310.
202. 赵其国. 21 世纪土壤科学展望. 地球科学进展, 2001, 16(5):704-709.
203. 赵其国, 周建民. 为 21 世纪土壤科学的创新发展作出新的贡献-参加第 17 届国际土壤学大会综述. 土壤, 2002, 5:237-256..
204. 张朝生, 陶澍, 袁贵平, 刘永. 天津市平原土壤元素含量的空间自相关研究. 土壤学报, 1995, 32 (1):50-57.
205. 张辉, 马东升. 长江(南京段)现代沉积物中重金属的分布特征及其形态研究. 环境化学, 1997, 16(5):429-434.
206. 张辉, 马东升. 南京地区土壤沉积物中重金属形态研究. 环境科学学报, 1997, 17(3):346-352.
207. 张朝生, 章申, 何建邦. 长江水系沉积物重金属含量空间分布特征研究——地统计学方法. 地理学报, 1997, 52 (2):184-192.
208. 张辉, 马东升. 公路重金属污染的形态特征及其解吸、吸持能力探讨. 环境化学, 1998, 17(6):564-568.
209. 张辉. 重金属污染的地球化学研究——城市和区域性污染的特征与机制. 博士论文, 2001.
210. 张辉 . 土壤重金属污染中背景含量与污染叠加含量的区分 . 环境化学 , 2003,22(6):605-610.
211. 张中一, 朱长会. 南京菜地土壤中铅的形态及其含量相关性研究. 南京农专学报, 1997.1:12-15.
212. 中科院土壤背景值协作组. 土壤学报, 1979, 16 (4):379-388.
213. 周代华, 李学垣, 徐风琳. Cu2+在针铁矿表面吸附的红外光谱研究. 华中农业大学学报, 1996, 15 (2):153-156.
214. 朱丽, 童春发, 乐美麟. 南京大厂区林区土壤重金属铜、镍、锌、锰含量分析. 南京林业大学学报, 1999, 23(3):67-70.
215. 朱永豪, 邓仁达, 卢亚非, 等. 不同湿度条件下黄棕壤光谱反射率的变化特性及其遥感意义. 土壤学报, 1984, 21(2):194-201.
2. Alloway, B.J. Soil processes and behavior of metals. In: Alloway, B.J (ed.) Heavy metals in soils. New York, 1990.
3. Balsam, W.L.; Deaton, B.C. Sediment dispersal in the Atlantic Ocean: Evaluation by visible light spectra. Rev. Aquatic Sci, 1991, 4:411- 447.
4. Balsam, W.L.; Wolhart, R. Sediment dispersal in the Argentine Basin: evidence from visible light spectra. Deep-Sea Res, 1993, Part A, 40:1001-1031.
5. Baumgardner, M.F.; Kristof, S.; Johannsen, C.J., et al. Effects of organic matter on the multispectral properties of soils. Proc. Indiana Acad. Sci., 1970, 79:413 - 422.
6. Baugh, W.M.; Kruse, F.A.; Atkinson, W.W. Quantitative Geochemical Mapping of Ammonium Minerals in the Southern Cedar Mountains, Nevada, Using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ, 1998, 65: 292-308.
7. Bedidi, A.; Cervelle, B.; Madeira, J., et al. Moisture effects on visible spectral characteristics of lateritic soils. Soil Sci, 1992, 153:129-141.
8. Ben-Dor, E.; Banin, A. Visible and near infrared (0.4-1.1mm) analysis of arid and semi arid soils. Remote Sensing of Environment, 1994, 48:261-274.
9. Ben-Dor, E.; Banin, A. Near infrared analysis (NIRA) as a rapid method to simultaneously evaluate, several soil properties. Soil Science Society of American Journal, 1995, 59:364-372.
10. Ben-Dor, E. Quantitative Remote Sensing of Soil Properties. Advances in Agronomy, 2000, 75:173-243.
11. Bertsch, P.M.; Seaman, J.C. Characterization of complex mineral assemblages: implications for contaminant transport and environmental remediation. Proc Natl Acad Sci U S A, 1999, March 30, 96(7):3350-3357.
12. Bowers, S.S.; Hanks, R.J. Reflectance of radiant energy from soils. Soil Science, 1965, 100:130-138.
13. Brewer, M.; Scott, T. Concise Encyclopedia of Biochemistry. Walter de Gruyter, Berlin, New York, 1983.
14. Burns, R.G. Mineralogical Applications of Crystal Field Theory. Cambridge University Press, Cambridge, 1970.
15. Burns, R.G. Thermodynamic data from crystal field spectra. in Microscopic to Macroscopic. Atomic Environments to Mineral Thermodynamics (S.W. Kieffer and A. Navrotsky, eds.). Mineralogical Society of America. Published in Rev. Mineral., 1985a, 14:277-316.
16. Burns, R.G. Mineralogical Applications of Crystal Field Theory, Second Edition, Cambridge University Press, Cambridge, 1993.
17. Chabrillat, S.; Goetz, A.F.H.; Krosley, L., et al. Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution. Remote Sensing of Environment, 2002, 82:431-445.
18. Chapman, J.E.; Rothery, D.A.; Francis, P.W. Remote sensing of evaporite mineral zonation insalt flats (salars). Int. J. Remote Sens., 1989, 10:245-255.
19. Chlopecka, A.; Bacon, J.R.; Wilson, M.J., et al. Forms of cadmium, lead, and zinc in contaminated soils from southwest Poland. J. Environ. Qual, 1996, 25:69-79.
20. Christensen, P.R.; Bandfield, J.L.; Hamilton, V.E., et al. A thermal emission spectral library of rock-forming minerals. J. Geophys. Res., 2000, 105:9735-9739.
21. Chung, h.; Lee, J.S.; Ku, M.S. Feasibility of simultaneous measurement of Xylene Isomers and other hydrocarbons in p-Xylene production processes using near-infrared spectroscopy. Appl. Spectrosc. 1998, 52(6):885-889.
22. CIDA (Canada International Development Agency). An urbanized world, statement on sustainable cities. Ministry of Public Works and Government Services Canada, Canada. 1998.
23. Clark, I. Practical Geostatistics. London Applied Science Publishers, 1979.
24. Clark, R.N.; Roush, T.L. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res., 1984, 89:6329-6340.
25. Clark, R.N.; King, T.V.V.; Klejwa, M., et al. High Spectral Resolution Reflectance Spectroscopy of Minerals. J. Geophys Res., 1990a, 95:12653-12680.
26. Clark, R.N. Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In Manual of Remote Sensing, 1999.
27. Clark, R.N.; Swayze, G.A.; Livo, K.E., et.al. Imaging Spectroscopy: Earth and Planetary Remote Sensing with the USGS Tetracorder and Expert Systems. Journal of Geophysical Research-planets, 2003,108 (E12):Art. No. 5131 DEC 6.
28. Cloutis, E.A. Hyperspectral Geological Remote Sensing: Evaluation of Analytical Techniques. Int. J. Remote Sensing, 1996,17 (12):2215-2242.
29. Condit, H.R. The spectral reflectance of American soils. Photogrammetric Engineering, 1970, 36:955-966.
30. Corwin, D.L.; Wagenet, R.J. Applications of GIS to the modeling of non-point source pollutants in the vadose zone: A conference overview. Journal of Environmental Quality, 1996, 25(3):403-411.
31. Corwin, D.L.; Loague, K.; Ellsworth, T.R. GIS-based modeling of nonpoint source pollutants in the vadose zone. Journal of Soil and Water Conservation, 1998, 53 (1):34-38.
32. Crowley, J.K. Visible and near-infrared (0.4-2.5 mm) reflectance spectra of playa evaporite minerals. Journal of Geophysical Research, 1991, 96 (16):231-240.
33. Davies, B.E. Consequences of environmental contamination by lead mining in Wales. Hydrobiologia, 1987, 49:213.
34. Davis, A.P.; Upadhyaya, M. Desorption of camium from goethite. Wat. Res, 1996, 30(8):1894-1904.
35. Dehaan, R.L.; Taylor, G.R. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization. Remote Sens. Envir., 2002, 80:406- 417.
36. Dematte, J.A.M.; Camposa, R.C.; Alves, M.C., et al. Visible–NIR reflectance: a new approach on soil evaluation. Geoderma, 2004, 121:95-112.
37. Drake, N.A. Reflectance spectra of evaporite minerals (400-2500 nm): applications for remote sensing. Int. J. Remote Sens, 1995, 16:2555-2571.
38. Duke, E.F. Near Infrared Spectra of Muscovite, Tschermak Substitution, and Metamorphic Reaction Progress: Implications for Remote Sensing. Geology, 1994, 22:621-624.
39. Farmer, V.C. The Infra-Red Spectra of Minerals, (V.C. Farmer, ed.) Mineralogical Society,London, 1974.
40. Fidencio, P.H.; Poppi, R.J.; Andrade, J.C. Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy. Analytica Chimica Acta, 2002, 453:125-134.
41. Frangi, J.P.; Richard, D. Heavy metal soil pollution cartography in northern France. The Science of the Total Environment, 1997, 205:71-79.
42. Frazier, B.E.; Cheng, Y. Remote sensing of soils in the Eastern Palouse region with Landsat Thematic Mapper. Remote Sens. Envir., 1989, 28:317-325.
43. Gaffey, S.J. Spectral Reflectance of Carbonates Minerals in the Visible and Near Infrared (0.35-2.55 microns): Calcite, Aragonite, and Dolomite. American Mineralogist, 1986, 71:151-162.
44. Galvao, L.S.; Vitorello, I. Role of organic matter in obliterating the effects of iron on spectral reflectance and colour of Brazilian tropical soils. Int. J. Remote Sens, 1998, 19:1969-1979.
45. Galvao, L.S. Variations in Reflectance of Tropical Soils: Spectral-Chemical Composition Relationships from AVIRIS data. Remote sens. Envir., 2001, 75:245-255.
46. Gao, B.C.; Goetz, A.F.H.; Wiscombe, W.J. Cirrus cloud detection from airborne imaging spectrometer data using the 1.38mm water vapor band. Geophysical research letter, 1993b, 40(4):301-304.
47. Garcia-Sanchez, A.; Moyano, A.; Munez, C. Forms of cadmium, lead, and zinc in polluted mining soils and uptake by plants (Soria province, Spain). Commun.Soil Sci. Plant Anal, 1999, 30:1385-1402.
48. Gong, P.; Pu, R.; Heald, R.C. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. International Journal of Remote Sensing, 2002, 23 (9):1827-1850.
49. Grant, R.; Grant, C. Grant and Hackh's Chemical Dictionary. McGraw-Hill, New York, 1987.
50. Grossl, P.R.; Sparks, D.L. Evaluation of contaminant ion adsorption/desorption on goethite using pressure-jump relaxation kinetics. Geoderma, 1995, 67:87-101.
51. Hammaker, R.M.; Poholarz, J.M.; Marshall, T.L., et al. “Remote sensing of volatile organic compounds in the atmosphere using a Fourier transform near-infrared spectrometer,” Leaping Ahead Near-infrared Spectrosc., (Proc. Int. Conf. Near-infrared Spectrosc.), 6th EDITOR: Batten, G. D. (Ed.), 1995:510-513.
52. Hapke, B. Bidirectional Reflectance Spectroscopy, 1, Theory. Journal of Geophysical Research, 1981, 86:3039-3054.
53. Hapke, B. Theory of Reflectance and Emittance Spectroscopy. Cambridge University Press, 2005.
54. Hauff, P.L.; Peters, D.C.; Borstad, G.W., et al. Hyperspectral investigations of mine waste and abandoned mine lands - The Dragon calibration site case study. Summaries of the Ninth Annual JPL Airborne Earth Science Workshop, JPL, Pasadena, February, 2000:23-25.
55. Henderson, T.L.; Szilagyi, A.; Baumgardner, M.F., et al. Spectral band selection for classification of soil organic matter content. Soil Sci.Soc.Am. J., 1989, 53:1778-1784.
56. Hesham, A.S.; Watson, P.R. An XPS study of the adsorption of lead on goethite a-FeOOH. Applied Surface Science, 1998, 136:46-54.
57. Hoogenboom, H.J.; Dekker, A.G.; Althuis, I.J.A. Simulation of AVIRIS Sensitivity for Detecting Chlorophyll over Coastal and Inland Waters. Remote Sensing of Environment, 1998, 65:333-340.
58. Hunt, G.R.; Salisbury, J.W. Visible and near infrared spectra of minerals and rocks. I. Silicate minerals, Mod. Geology, 1970, 1:283-300.
59. Hunt, G.R.; Salisbury, J.W. Visible and near infrared spectra of minerals and rocks. II. Carbonates, Mod. Geology, 1971, 2:23-30.
60. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and nearinfrared spectra of minerals and rocks: IV. Sulphides and sulphates. Mod. Geol., 1971, 3:1-14.
61. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and near infrared spectra of minerals and rocks. III. Oxides and hydroxides, Mod. Geology, 1971a, 2:195-205.
62. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and near infrared spectra of minerals and rocks. IV. Sulphides and sulphates, Mod. Geology, 1971b, 3: 1-14.
63. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and near infrared spectra of minerals and rocks. V. Halides, arsenates, vanadates, and borates, Mod. Geology, 1972, 3:121-132.
64. Hunt, G.R.; Salisbury, J.W.; Lenhoff, C.J. Visible and near infrared spectra of minerals and rocks. VI. Additional silicates, Mod. Geology, 1973, 4:85-106.
65. Idso, S.B.; Jackson, R.D.; Reginato, R.J., et al. The dependence of bare soil albedo on soil water content. Journal of Applied Meteorology, 1975, 14:109-113.
66. Irons, J.R.; Weismiller, R.A.; Petersen, G.W. Soil reflectance. In: Theory and application of optical remote sensing (Asrar G. ed.). New York: John Wiley and Sons, 1989:66-106.
67. Islam, K.; Singh, B.; McBratney, A.B. Simultaneous estimation of several soil properties by ultra-violet, visible and near infrared reflectance spectroscopy. Australian Journal of Soil Research, 2003, 41:1-14.
68. Ji, J.F.; Balsam, W.L.; Chen, J., et al. Rapid and quantitative measurement of hematite and goethite in the Chinese Loess-Paleosol sequence by diffuse reflectance spectroscopy. Clays and Clay Miners, 2002, 50:210-218.
69. Ji, J.F.; Chen, J.; Jin, L., et al. Relating magnetic susceptibility (MS) to the simulated thematic mapper (TM) bands of the Chinese loess: Application of TM image for soil MS mapping on Loess Plateau. J. Geophys. Res., 2004, 109 (B5):Art. No. B05102.
70. Johnson, B.B. Effects of pH, temperature and concentration on the adsorption of cadmium on goethite Environ. Sci. Technol., 1990, 24(1):112-116.
71. Johnson, P.E.; Smith, M.O.; Adams, J.B. Simple Algorithms for Remote Determination of Mineral Abundances and Particles Sizes from Reflectance Spectra. Journal of Geophysical Research, 1992, 97 (E2):2649-2657.
72. Kaiser, J. Toxicologists shed new light on old poisons. Science, 1998, 279(5358):1850-1851.
73. Karnieli, A.; Tsoar, H. Spectral reflectance of biogenic crust developed on desert dune sand along the Israel-Egypt border. Int. J. Remote Sens., 1995, 16:369-374.
74. Karnieli, A.; Sarafis, V. Reflectance spectrophotometry of cyanobacteria within soil crusts-a diagnostic tool. Int. J. Remote Sens., 1996, 17 (8):1609-1614.
75. Karnieli, A. Development and implementation of spectral crust index over dune sands. Int.J. Remote Sens., 1997, 18:1207-1220.
76. Kemper, T.; Sommer, S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environmental science and technology, 2002, 36:2742-2747.
77. Khawlie, M.; Awad, M.; Shaban, A., et al. Remote sensing for environmental protection of the eastern Mediterranean rugged mountainous areas, Lebano. ISPRS Journal ofPhotogrammetry & Remote Sensing, 2002, 57:13-23.
78. Kimbrough, D.E; Cohen, Y.; Winer, A.M., et al. A critical assessment of chromium in the Environment. Critical Reviews in Environmental Science and Technology, 1999, 29(1):1-46.
79. Knapp, E.P.; Herman, J.S.; Hornberger, G.M., et al. The effect of distribution of iron-oxyhydroxide grain coatings on the transport of bacterial cells in porous media. Environ. Geol., 1998, 33:243-248.
80. Kooistra, L.; Wehrens, R.; Leuven, R.S.E.W., et al. Possibilities of visible near infrared spectroscopy for the assessment of soil contamination in river floodplains. Analytica Chimica Acta, 2001, 446:97-105.
81. Kubelka, P.; Munk, F. Ein Beitrag zur Optik der Farbanstriche. Z. Tech. Phys., 1931, 11:593-603.
82. Lee, T.C.; Tanaka, N.; Lamb, P.W., et al. Induction of gene amplification by arsenic. Science, 1998, 241:79-81.
83. Leeman, V. The NASA Earth resources spectral information system. A data compiletion. Michgan University, 1971.
84. Lévesque, J.; Neville, R.; Staenz, K. Truong, Q. Preliminary Results on the Investigation of Hyperspectral Remote Sensing for the Identification of Uranium Mine Tailings; Proceedings of ISSSR'01, Québec City, Canada, June, 2001:10-15.
85. Levine, E.R.; Kimes, D.S. Predicting soil carbon in Mollisols using neural networks, In: Soil Processes and the Carbon Cycle. Edited by Rattan Lal, CRC Press, 1998:473-484.
86. Li, X.D.; Coles, B.J.; Ramsey, M.H., et al. Sequential extraction of soils for multielement analysis by ICP-AES. Chemical Geology, 1995, 124:109-123.
87. Li, X.D.; Poon, C.S.; Liu, P.S. Heavy metal contamination of urban soils and street dusts in Hong Kong. Applied Geochemistry, 2001, 16:1361-1368.
88. Liu, W.D.; Baret, F.; Gu, X..F., et al. Evaluation of methods for soil surface moisture estimation from reflectance data. Int. J. Remote Sens., 2003, 24 (10):2069-2083.
89. Madeira, J.; Bedidi, A.; Cervelle, B., et al. Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: the application of a Thematic Mapper (TM) image for soil mapping in Brasilia, Brazil. Int. J. Remote Sens., 1997, 18:2835-2852.
90. Madrid, L.; Diaz-Barrientos, E.; Madrid, F. Distribution of heavy metal contents of urban soils in parks of Seville. Chemosphere, 2002, 49:1301-1308.
91. Malley, D.F.; Williams, P.C. Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environ. Sci. Technol., 1997, 31:3461-3467.
92. Martens, H. Reliable and relevant modeling of real world data: a personal account of the development of PLS Regression. Chemometrics and Intelligent Laboratory Systems, 2001. 58:85–95.
93. Meera, F.V.D. Analysis of spectral absorption features in hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 2004, 5:55-68.
94. Miller, J.R.; Lechler, P.J.; Desilets, M. The role of geomorphic processes in the transport and fate of mercury in the Carson Rover basin, west-central Nevada. Environmental geology, 1998, 33(4):249-262.
95. MINDEC. Ministerial Declaration of the Fourth International Conference on the Protection of the North Sea. Esjberg, Denmark, June 1995:8-9.
96. Montgomery, O.L. An Investigation of the Relationship between Spectral Reflectance and the Chemical, Physical, and Genetic Characteristics of Soils. Ph.D. thesis, Purdue University, West Lafayette, Indiana, 1976.
97. Moran, P.A.P. Notes on continuous stochastic phenomena. Biometrika, 1950, 37:17-23.
98. Mossop, K.F.; Davidson, C.M. Comparison of original and modified BCR sequential extraction procedures for the fractionation of copper, iron, lead, manganese and zinc in soils and sediments. Analytica Chimica Acta, 2003, 478:111-118.
99. Moulder, J.F.; Stickle, W.F.; Sobol, P.E., et al. Handbook of Photoelection Spectroscopy, Perkin-Elmer. Co. Physical Electronics Divisions USA, 1992.
100. Muller, G. Index of geoaccumulation in sediments of the Rhine River. Geojournal, 1969, 2(3):108-118.
101. Muller, E.; Decamps, H. Modeling soil moisture reflectance. Remote Sens. Envir., 2000, 76:173-180.
102. Murray, I. Aspects of interpretation of NIR spectra, in Analytical Application of Spectroscopy (C.S. Creaser and A.M.C. Davies, Eds.), Royal Society of Chemistry, London, 1988:9-21.
103. Mustard, J.F.; Pieters, C.M. Photometric Phase Functions of Common Geologic Minerals and Applications to Quantitative Analysis of Mineral Mixture Reflectance Spectra. Journal of Geophysical Research, 1989, 94 (B10):13619-13634.
104. Mustard, J.F.; Sunshine, J.M. Mineral chemistry from reflectance application of the MGM to minerals of the tremolite-ferroactinolite solid solution series. EOS, 1992, 73:187.
105. Naidu, R.; Sumner, M.E.; Harter, R. Sorption of heavy metals in strongly weathered soils: an overview. Environmental Geochemistry and Health, 1998, 20:5-9.
106. Nickson, R.; Mcarthur, J.; Burgess, W., et al. Arsenic poisoning of Bangladesh groundwater. Nature, 1998, 395:338.
107. Norris, K.H.; Williams, P.C. Optimization of mathematical treatment of raw near-infrared signal in the measurements of protein in hard red spring wheat. I: Influence of particle size, Cer. Chem., 1984, 61:158-165.
108. Obukhov, A.I.; Orlov, D.C. Spectral reflectivity of the major soil groups and possibility of using diffuse reflection in soil investigations. Pochvovedeniye, No.2, 1964.
109. Oliveira, S.M.B.D. Soils as an important sink for mercury in the Amazon. Water, Air Soil Pollut., 2001, 26:321-337.
110. Peacock, C.L.; Sherman, D.M. copper sorption onto goethite, hematite and lepidocrocite: a surface complexation model based on ab initio molecular geometries and EXAFS spectroscopy. Geochimica et Cosmochimica Acta, 2004, 68(12):2623-2637.
111. Pieters, C.M.; Englert, P.A. Remote Geochemical Analysis: Elemental and Mineralogical Composition. Cambridge University Press, 1993.
112. Pinker, R.T.; Karnieli, A. Characteristic spectral reflectance of semi-arid environment. Int. J. Remote Sens., 1995, 16:1341-1363.
113. Pueyo, M.; Sastre, J.; Hernández, E., et al. Prediction of Trace Element Mobility in Contaminated Soils by Sequential Extraction. J. Environ. Qual., 2003, 32:2054-2066.
114. Querauviller, P.H.; Rauret, G.; Griepiuk, B. Single and sequential extraction in sediments and soils. International Journal of Environmental Analytical Chemistry, 1993, 51:231-240.
115. Ramos, L.; Hermamdez, L.M.; Gonzalez, J. Sequential fractionation of Copper, Lead, Cadmium and Zinc in soils from or near Donana Nationl Park. J. Environ. Qual., 1994,23:50-57.
116. Rauret, G.; Lopez-Sanchez, J.F.; Sahuquillo, A., et al. Improvement of the BCR three step sequential extraction procedure prior to the certification of new sediment and soil reference materials. Journal of Environmental Monitoring, 1999, 1(1):57-61.
117. Reeves III, J.B.; McCarty, G.W.; Mimmo, T. The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environmental Pollution, 2002, 116,S277-S284.
118. Sadtler. Minerals: Infrared grating spectra. Sadtler research laboratories, Pennsylvania, 1973.
119. Sahuquilloa, A.; Lopez-Sanchez, J.F.; Rubioa, R., et al. Use of a certified reference material for extractable trace metals to assess sources of uncertainty in the BCR three-stage sequential extraction procedure. Analytica Chimica Acta, 1999, 382:317-327.
120. Scheinost, A.C.; Ford, R.G.; Sparks, D.L. The role of Al in the formation of secondary Ni precipitates on pyrophyllite, gibbsite, talc, and amorphous silica: A DRS study. Geochimica et Cosmochimica Acta, 1999, 63:3193-3203.
121. Schwertmann, U. Some properties of soil and synthetic iron oxides. p. 203–250. In J.W. Stucki et al. (ed.) Iron in soils and clay minerals. D. Reidel, Dordrecht, the Netherlands, 1988
122. Schwertmann, U.; Cornell, R.M. Iron Oxides in the Laboratory; Preparation and Characterization. Weinheim, New York, 1991.
123. Scott, J.S.; Smith, P.G. Dictionary of Waste and Water Treatment. Butterworths, London, 1981.
124. Shang, J.; Lévesque, J.; Howarth, P., et al. Preliminary Investigation of Acid Mine Drainage Detection Using casi Data, Copper Cliff, Ontario, Canada; Fourth International Airborne Remote Sensing Conference and Exhibition/21st Canadian Symposium on Remote Sensing, Ottawa, Ontario, Canada, 21-24 June, 1999:771-778.
125. Shenk, J.S.; Westerhaus, M.O. Analysis of Agriculture and food Products by Near-Infrared Reflectance Spectroscopy; Monograph; Infrasoft International: Port Matilda, PA, 1993:103.
126. Sherman, D.M.; Waite, T.D. Electronic spectra of Fe3+ oxides and oxide hydroxides in the near IR to near UV. Amer. Mineral, 1985, 70:1262-1269.
127. Soils Survey Staff, Bureau of Plant Industry: “Soil Survey Manual”. Handbook No. 18, U. S. Department of Agriculture, Washington, D.C., 1951.
128. Sparks, D. L. Environmental Soil Chemistry. Academic Press, San Diego, 1995.
129. Stoner, E.R.; Baumgardner, M.F. Characteristic variations in reflectance on surface soils, Soil Sci. Soc. Am. J., 1981, 45:1161-1165.
130. Sunshine, J.M.; Pieters, C.M.; Pratt, S.F. Deconvolution of mineral absorption bands: an improved approach. Journal of Geophysical Research, 1990, 95 (B5):6955-6966.
131. Sunshine, J.M.; Pieters, C.M. Determining the Composition of Olivine from Reflectance Spectroscopy. Journal of Geophysical Research, 1998, 103 (E6):13675-13688.
132. Swayze, G.A.; Clark, R.N.; Pearson, R.M., et al. Mapping acid-generating minerals at the California Gulch Superfund Site in Leadville, Colorado using imaging spectroscopy: Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, JPL Publication, 1996:96-4, vol. 1, March 4-8, 231-234.
133. Swayze, G.A.; Clark, R.N.; Hageman, P.L., et al. Using imaging spectroscopy to cost-effectively locate acid-generating minerals at mine sites: an example from the California Gulch Superfund site at Leadville, Colorado. Summaries of the Seventh Annual JPL AirborneEarth Science Workshop, JPL, Pasadena, 12-I 6 January, 1998.
134. Swayze, G.A.; Smith, K.S.; Clark, R.N., et al. Using Imaging Spectroscopy to Map Acidic Mine Waste", Environmental Science and Technology, 2000, 34:47-54.
135. SWES 453/553. Soil Optical Properties and Monitoring Soil Processes. http://tbrs.arizona.edu/education/553/index.htm.
136. Tessier, A.; Campbell, P.G.C.; Bisson, M. Sequential extraction procedure for the speciation of particulate trace metals. Anal. Chem., 1979, 51:844-851.
137. Uhlenbrock, S.; Scharfschwerdt, C.; Neumann, M., et al. The influence of defects on the Ni 2p and O1s xps of NiO. J. Phys. Condens. Matter, 1992:7973-7978.
138. Ure, A.M.; Quevauviller, P.; Muntau, H., et al. Speciation of heavy metals in soils and sediments——an account of the improvement and harmonization of extraction techniques undertaken under the auspices of the BCR of the commission-of-the-European-Communities. International Journal of Environmental Analytical Chemistry, 1993, 51 (1-4):135-151.
139. Ure, A.M.; Davison, C.M.E. Chemical Speciation in the Environment. Blackie Academic and Professional, London, 1995.
140. USPHS. Toxicological profile on CD-ROM. Agency for Toxic Substances and Disease Registry, 1997.
141. USPHS. 8TH Report on Carcinogens 1998 Summary, 1998.
142. Van, D.B.W.; Wienke, D.; Melssen, W., et al. Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS). Chemometrics and Intelligent Laboratory Systems, 1996, 35:187-197.
143. Watson, S.M.; Kroutil, R.T.; Traynor, C.A., et al. “Remote-sensing and in situ atmospheric chemistry studies with the use of a manned hot-air balloon platform,” Field Anal. Chem., Technol., (Field Analytical Chemistry and Technology), 1996, 1(1):13-22.
144. Wilcox, C.H.; Frazier, B.E.; Ball, S.T. Relationship between soil organic carbon and Landsat-Tm data in Eastern Washington. Photogrammetric Engineering and Remoter Sensing. 1994, 60 (6):777-781.
145. Wold, S.; Martens, H.; Wold, H. The multivariate calibration method in chemistry solved by the PLS method. In: Ruhe, A. and Kagstrom, B. (Eds.), Proc. Conf. Matrix Pencils, Lecture Notes in Mathematics, Springer-Verlag, Heidelberg, 1983:286-293.
146. Wong, S.C.; Li, X.D.; Zhang, G., et al. Heavy metals in agricultural soils of the Peral River Delta, South China. Environmental Pollution, 2002, 119:33-44.
147. Xu, F.L.; Zhou, D.H.; Cheng, X.Q., et al. Influence of phosphate on Cu ion adsorption desorption by goethite. 17th WCSS, Thailand, August 2002:14-21.
148. Yu, K.C.; Tsai, L.J.; Chen, S.H., et al. Correlation analyses on binding behavior of heavy metals with sediment matrices. Wat. Res., 2001, 35(10):2417-2428.
149. Zhang, C.S.; Olle, S. Statistics and GIS in environmental geochemistry-some problems and solutions. Journal of Geochemical Exploration, 1998, 64:339-353.
150. 陈怀满, 等. 土壤-植物系统中的重金属污染. 北京: 科学出版社, 1996, 7-9.
151. 陈晋阳, 黄卫. 无定形氢氧化铁吸附水溶液中镉离子机理的 XPS 研究. 分析测试学报, 2002, 21(3):70-72.
152. 成杭新, 杨忠芳, 奚小环, 等. 长江流域沿江镉异常示踪与追源的战略与战术. 第四纪研究, 2005, 25(3):285-290.
153. 戴昌达. 中国主要土壤光谱反射特性分类与数据处理的初步研究. 见:遥感文选. 北京:科学出版社, 1981.
154. 戴前进. 土壤汞的地球化学行为及其污染的防治对策. 地质地球化学, 2002, 30(4): 75-79.
155. 地质情报研究所. 遥感专辑, 第一辑. 北京: 地质出版社, 1980.
156. 丁爱芳, 潘根兴. 南京城郊零散菜地土壤与蔬菜重金属含量及健康风险分析. 生态环境, 2003, 12(4):409-411.
157. 方海兰, 徐凤琳, 刘凡, 等. 中南地区几种地带性土壤中氧化铁类型与钼吸附特性. 热带亚热带土壤科学, 1997, 6(3):182-186.
158. 高小杰. 城郊土壤-蔬菜系统污染评价研究. 江苏环境科技, 1997, 2:16-20.
159. 甘甫平, 刘圣伟, 周强. 德兴铜矿矿山污染高光谱遥感直接识别研究. 地球科学, 2004,29 (1):119-126.
160. 郭进义, 杨忠芳. 浅议地球化学的外延. 地球科学进展, 1998, 13 (1):78-80.
161. 何挺. 土地质量高光谱遥感监测方法研究. 博士论文, 2003.
162. 何延波, 杨琨, 侯英雨. 浅谈遥感地球化学. 地质地球化学, 1997, 4:98-103.
163. 侯景儒, 黄竞先. 地质统计学的理论与方法. 北京: 地质出版社, 1990.
164. 江苏省农业厅. 江苏省农业环境质量报告书. 南京: 江苏省农业厅出版, 1990.
165. 李德仁. 论 21 世纪遥感与 GIS 的发展. 武汉大学学报(信息科学版), 2003, 28(2):127-131.
166. 李克斌, 刘维屏, 邵颖. 重金属离子在腐殖酸上吸附的研究. 环境污染与防治, 1997,
19(1):9-11.
167. 廖启林, 吴新民, 翁志华, 等. 南京地区多目标地球化学调查基本成果及其相关问题初探. 中国地质, 2004, 31(1):70-77.
168. 刘凡, 徐凤琳, 李学垣, 等. 几种可变电荷土壤中氧化铁类型与磷吸附特性. 华中农业大学学报, 1993, 12(5):450-454.
169. 刘芬, 陈萦. 含铁化合物的 Fe 2p 和 Fe3s 电子能谱研究. 分析测试技术与仪器, 2001, 7(3):166-169.
170. 刘伟东. 高光谱遥感信息提取与挖掘研究. 博士论文, 2002.
171. 刘玉洁, 杨忠东, 等. MODIS 遥感信息处理原理与算法. 北京: 科学出版社, 2001:2-12.
172. 陆常青. 土壤粘粒的红外光谱. 土壤, 1986, 18 (2):94-100.
173. 卢瑛, 龚子同, 张甘霖. 南京城市土壤中重金属的化学形态分布. 环境化学, 2003, 22(2):131-136.
174. 马毅杰. 长江中下游土壤矿物组成与其土壤肥力. 长江流域资源与环境, 1994, 3(1):1-8.
175. 彭文世, 刘高魁. 矿物红外光谱图集. 科学出版社, 1982.
176. 浦瑞良, 宫鹏. 高光谱遥感及其应用. 北京: 高等教育出版社, 2000:193-202.
177. 任若恩, 王惠文. 多元统计数据分析. 北京: 国防工业出版社, 1997.
178. 斯韦恩, P. H.; 戴维主, S. M. 遥感定量方法. 北京: 科学出版社, 1984, 153-160.
179. 孙波, 周生路, 赵其国. 基于空间变异分析的土壤重金属复合污染研究. 农业环境科学学报, 2003, 22(2):248-251.
180. 唐诵六. 环境中若干元素的自然背景值及研究方法: 南京地区土壤重金属浓度的概率分布. 中国科学院环境背景值学术交流会论文文集, 1982:9-15.
181. 田国良. 土壤水分的遥感监测方法. 环境遥感, 1991, 6(2):89-98.
182. 王海, 王春霞, 王子健. 太湖表层沉积物中重金属的形态分析. 环境化学, 2002, 21(5):430-435.
183. 王浩. 南京地区城市土壤背景值研究及环境质量分析. 硕士论文, 2003.
184. 王润生, 等. 成像光谱方法技术开发应用研究. 国土资源部“九五”重点科研项目报告, 航空物探遥感中心, 1999.
185. 王绍庆. 土壤和水体反射光谱特性及其应用. 见:童庆禧等主编.中国典型地物波谱及其特征分析. 北京: 科学出版社, 1990:611-618.
186. 王学军, 李本纲, 陶澍. 土壤微量金属含量的空间分析. 北京: 科学出版社, 2005.
187. 王玉, 张一平, 陈思根. 中国 6 种地带性土壤红外光谱特征研究. 西北农林科技大学学报, 2003, 31 (1):57-61.
188. 吴新民, 李恋卿, 潘根兴, 等. 南京市不同功能城区土壤中重金属 Cu、Zn、Pb 和 Cd 的污染特征. 环境科学, 2003, 24(3):105-111.
189. 吴昀昭, 田庆久, 季峻峰, 等. 遥感地球化学研究. 地球科学进展, 2003, 18 (2):228-235.
190. 夏增禄. 中国土壤环境容量. 北京: 地震出版社, 1992.
191. 邢光熹, 朱建国. 土壤微量元素和稀土元素化学. 北京: 科学出版社, 2003.
192. 徐彬彬, 等. 土壤光谱反射特性与理化性状的相关分析. 土壤专报, 第 41 号, 科学出版社, 1986.
193. 徐彬彬, 季耿善. 宁芜土壤遥感研究的主要成果. 土壤专报, 1987, 41 号:5-8.
194. 徐彬彬等. 中国陆地背景和土壤光谱反射特性的地理分区的初步研究. 环境遥感, 1991.
195. 徐冠华. 论热红外遥感中的基础研究. 中国科学(E), 2000, 30, 1-5.
196. 徐瑞松. 遥感生物地球化学. 广东科技出版社, 2003.
197. 杨学义. 环境中若干元素的自然背景值及研究方法: 南京地区土壤背景值与母质的关系. 中国科学院出版(中科院环境背景学术交流会论文文集), 1982:16-20.
198. 杨忠芳, 奚小环, 成杭新, 等. 区域生态地球化学评价核心与对策. 第四纪研究, 2005, 25(3):275-283.
199. 俞天智, 滕秀兰, 张子瑜, 等. 用荧光光谱研究腐殖酸与金属离子 Al 的配合作用. 环境化学, 1999, 18(6):557-560.
200. 赵其国. 为跨世纪土壤学的发展作出新贡献——第 15 届国际土壤学会会议综述. 土壤学报, 1995, 32(1):1-13.
201. 赵其国. 土壤与环境问题国际研究概况及其发展趋向——参加第 16 届国际土壤学会专题综述. 土壤, 1998, 6:281-310.
202. 赵其国. 21 世纪土壤科学展望. 地球科学进展, 2001, 16(5):704-709.
203. 赵其国, 周建民. 为 21 世纪土壤科学的创新发展作出新的贡献-参加第 17 届国际土壤学大会综述. 土壤, 2002, 5:237-256..
204. 张朝生, 陶澍, 袁贵平, 刘永. 天津市平原土壤元素含量的空间自相关研究. 土壤学报, 1995, 32 (1):50-57.
205. 张辉, 马东升. 长江(南京段)现代沉积物中重金属的分布特征及其形态研究. 环境化学, 1997, 16(5):429-434.
206. 张辉, 马东升. 南京地区土壤沉积物中重金属形态研究. 环境科学学报, 1997, 17(3):346-352.
207. 张朝生, 章申, 何建邦. 长江水系沉积物重金属含量空间分布特征研究——地统计学方法. 地理学报, 1997, 52 (2):184-192.
208. 张辉, 马东升. 公路重金属污染的形态特征及其解吸、吸持能力探讨. 环境化学, 1998, 17(6):564-568.
209. 张辉. 重金属污染的地球化学研究——城市和区域性污染的特征与机制. 博士论文, 2001.
210. 张辉 . 土壤重金属污染中背景含量与污染叠加含量的区分 . 环境化学 , 2003,22(6):605-610.
211. 张中一, 朱长会. 南京菜地土壤中铅的形态及其含量相关性研究. 南京农专学报, 1997.1:12-15.
212. 中科院土壤背景值协作组. 土壤学报, 1979, 16 (4):379-388.
213. 周代华, 李学垣, 徐风琳. Cu2+在针铁矿表面吸附的红外光谱研究. 华中农业大学学报, 1996, 15 (2):153-156.
214. 朱丽, 童春发, 乐美麟. 南京大厂区林区土壤重金属铜、镍、锌、锰含量分析. 南京林业大学学报, 1999, 23(3):67-70.
215. 朱永豪, 邓仁达, 卢亚非, 等. 不同湿度条件下黄棕壤光谱反射率的变化特性及其遥感意义. 土壤学报, 1984, 21(2):194-201.