排序方式： 共有4条查询结果，搜索用时 15 毫秒
1
1.
广州市万亩果园土壤重金属污染调查与评价 总被引：2，自引：0，他引：2
运用实地调查采样及地积累指数法、单因子污染指数法、综合污染指数法等方法对广州市万亩果园土壤的重金属（Cd、Pb、Cu、Zn）污染情况进行了调查分析与评价．结果表明：万亩果园土壤中Cd、Cu污染不容忽视,其中Cd污染最为严重,Cu、Zn次之,Pb污染较少;在《广州市海珠区果树保护区总体规划》划分的不同等级保护区中,针对研究区域,一级保护区土壤重金属综合污染指数为0．732,属较清洁（警戒线）等级,二级保护区土壤重金属综合污染指数为1．792,属轻度污染等级,其中Cd为主要污染物,其次是Cu;三级保护区Cd污染严重,单因子污染指数高达6．390,土壤重金属综合污染指数为4．699,属于重污染等级．在垂直分布上,随土层深度增加,万亩果园土壤重金属含量呈现Cd递增,Cu、Zn递减,Pb先增后减的规律;一级保护区以农业污染源为主,二级保护区农业污染源与工业污染源并存,三级保护区以工业污染源为主．通过在轻污染区添加石灰、羟基磷灰石等化学修复剂,在重污染区积极调整种植结构,发展清洁生产,防止土壤重金属通过食物链危害人体健康． 相似文献
2.
3.
Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High Fβ values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar Fβ values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards. 相似文献
4.
A laboratory batch study on arsenic sorption and desorption on guava orchard soils of Baruipur,West Bengal,India 总被引：1，自引：0，他引：1
Sudip Jyoti Sahu Bibhash Nath Sharmi Roy Biswapati Mandal Debashis Chatterjee 《Journal of Geochemical Exploration》2011
Groundwater contaminated with arsenic (As), when extensively used for irrigation, causes potentially long term detrimental effects to surface soils. Such contamination can also directly affect human health when irrigated crops, such as rice, vegetable and fruits, are used for human consumption. Therefore, an understanding of the sorption and desorption behavior of As in surface soils is of high importance, because these processes regulate the bioavailability of As in the soil environment. In this study, we have collected soils from guava orchards of Baruipur, West Bengal, and characterized soil chemistry and batch sorption and desorption behavior in the laboratory. The sorption and desorption behavior of As in the soils were examined using the Langmuir and Freundlich sorption equation. Regression analysis of the soil chemical characteristics and sorption equation parameters were also performed. The results suggest that the sorption behavior of arsenate is highly dependent on soil characteristics, specifically organic carbon, clay and Al2O3 content of the soils. Whereas desorption behavior is critically influenced by the presence of high concentrations of amorphous and/or crystalline Fe2O3 in the soils. Retention of the significant portion of As in the soils (~ 84% of the total) suggests that As in the orchard soils may not be highly bioavailable to plants for uptake. However, more detailed studies will be required to ascertain the role of individual soil components on the As sorption and desorption processes. 相似文献
1