Determination of Impact of Urbanization on Agricultural Land and Wetland Land Use in Balçovas’ Delta by Remote Sensing and GIS Technique

Because of their intense vegetation and the fact that they include areas of coastline, deltas situated in the vicinity of big cities are areas of greet attraction for people who wish to get away from in a crowded city. However, deltas, with their fertile soil and unique flora and fauna, need to be protected. In order for the use of such areas to be planned in a sustainable way by local authorities, there is a need for detailed data about these regions. In this study, the changes in land use of the Balçova Delta, which is to the immediate west of Turkey’s third largest city Izmir, from 1957 up to the present day, were investigated. In the study, using aerial photographs taken in 1957, 1976 and 1995 and an IKONOS satellite image from the year 2005, the natural and cultural characteristics of the region and changes in the coastline were determined spatially. Through this study, which aimed to reveal the characteristics of the areas of land already lost as well as the types of land use in the Balçova delta and to determine geographically the remaining areas in need of protection, local authorities were provided with the required data support. Balçova consists of flat and fertile wetland with mainly citrus-fruit orchards and flower-producing green houses. The marsh and lagoon system situated in the coastal areas of the delta provides a habitat for wild life, in particular birds. In the Balçova Delta, which provides feeding and resting for migratory birds, freshwater sources are of vital importance for fauna and flora. The settlement area, which in 1957 was 182 ha, increased 11-fold up to the year 2005 when it reached 2,141 ha. On the other hand, great losses were determined in farming land, olive groves, forest and in the marsh and lagoon system. This unsystematic and rapid urbanization occurring in the study region is not only causing the loss of important agricultural land and wetland, but also lasting water and soil pollution.     

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.     

硝化污泥低温冲击强化试验

采用完全好氧式膜生物反应器富集耐低温硝化污泥,通过低温冲击强化试验研究硝化污泥的耐低温特性.结果表明,低温对硝化污泥中微生物的群落多样性影响较大,温度越低,微生物多样性越低.低温强化组投加了耐低温高硝化菌含量硝化污泥,使得硝化菌在反应器内生长趋势良好,对低温冲击的恢复更有效果,且低温硝化污泥比中温硝化污泥具有更好的氨氮...     

人工湿地污水处理系统脱氮研究进展

系统地介绍了人工湿地去除污水中氮污染物的机理及国内外研究进展，详细阐述了影响人工湿地污水处理系统脱氮的内、外界因素，对于人工湿地污水处理工艺的推广应用提供科学依据。     

基于TM影像的乌梁素海叶绿素a浓度反演

首先选取预处理后乌梁素海TM影像的单波段及波段组合与实测叶绿素a浓度进行分析,发现相关性很低。然后,结合乌梁素海的水利条件和自身特点,把湖区分为两个区,发现波段组合(TM1+TM2+TM4)/TM3和TM4/TM3与叶绿素a浓度有较高的相关性。最后,选取误差较低的波段组合(TM1+TM2+TM4)/TM3建立线性模型,实时、快速地反演乌梁素海湖区叶绿素a浓度,与实地情况吻合,为乌梁素海"黄苔"预警提供理论依据。     

广州城市污泥中重金属形态特征及其生态风险评价

分析了广州市4个不同来源的城市污水处理污泥中重金属含量,考察了污泥样品中重金属形态分布和生物可利用性,并分别利用风险评价指数(RAC)和固废重金属毒性浸出方法评价了污泥中重金属生态危害风险和浸出毒性风险.结果表明,污泥样品中Cu、Cr、Pb和Zn含量较高,不同来源污水处理污泥中重金属含量差别较大.污泥样品中重金属绝大部分以非稳定态存在,酸性污泥中可迁移的酸溶态重金属比例较高.由单一萃取结果,1 mol·L-1NaOAc溶液(pH 5.0)和0.02 mol·L-1EDTA+0.5 mol·L-1NH4OAc溶液(pH 4.6)分别对酸性和碱性污泥中生物可利用态重金属具有较好的萃取能力.污泥酸性越强,其中生物可利用态重金属比例越大.污泥中重金属的迁移能力使其处于高生态危害风险程度;重金属的生物可利用性使酸性污泥大多处于极高危害风险程度,而使碱性污泥大多处于中等危害风险水平.除城市污水处理污泥外,污泥样品中重金属具有高的浸出毒性风险,萃取重金属生物可利用态后,污泥仍具有高浸出毒性风险,但由于浸出毒性风险降低使部分污泥可进行填埋处置.     

中国水土保持生态服务功能价值估算及其空间分布

水土资源是人类赖以生存的重要的物质基础,水土保持作为人类对生态系统干预的有效手段,其生态服务功能价值也受到越来越多的重视。论文选取土壤保持价值和涵养水源价值两项指标,结合遥感数据、气象数据以及地面数据,对原有的基于遥感的水土保持生态服务功能价值估算模型进行改进,以期更好地体现水土保持生态服务功能应有的价值。论文运用改进后的模型估算2001年全国水土保持生态服务功能价值达16 760.55×108元,基本呈自东南向西北递减趋势。     

不同配置人工湿地对分散性生活污水的处理效能研究

为研究不同植物及组合(美人蕉+黄菖蒲、花叶芦竹、黄菖蒲、旱伞草)与不同填料(陶粒、沸石、改性沸石、改性钢渣)对系统处理分散性生活污水效能的影响,建立了8种不同配置的潜流式人工湿地中试系统。结果表明,各湿地对COD均具有较好的去除效果(去除率大于70%)。旱伞草、黄菖蒲+美人蕉、黄菖蒲和花叶芦竹四植物系统对TN的去除率均呈先降低后上升的趋势,最终平均去除率分别为57.8%、51.1%、50.4%和45.5%。改性沸石、沸石、陶粒和改性钢渣填料系统对TN平均去除率分别为96.4%、83.7%、34.6%和26.6%。各植物系统对TP的去除效果差异不显著,其中旱伞草系统TP平均去除负荷最大(0.63 g/(d·m2))。各填料对TP的去除效果从大到小依次为改性钢渣、改性沸石、陶粒、沸石,改性钢渣和改性沸石对TP的去除能力显著高于陶粒和沸石,平均去除率达86.3%和78.8%。研究表明,分别采用旱伞草和改性沸石为湿地植物和填料具有较好的应用前景。     

贵阳城市污水及南明河中氯霉素和四环素类抗生素的特征

采用固相萃取-高效液相色谱（UV检测）分析了贵阳城市污水、南明河水和沉积物中氯霉素和四环素类抗生素的特征.结果显示,南北两岸污水中氯霉素、土霉素、四环素和金霉素的平均含量分别为27.0、 2.3、 11.0、 1.1 μg·L^-1和21.2、 2.1、 9.5、 0.5 μg·L^-1,其中以氯霉素的污染为主;污水中抗生素的含量呈现明显的季节变化,这与用水量和疾病特点有关.南明河已广泛受到包括农业、养鱼塘、城市污水等来源的抗生素污染,其中城市污水是最重要的来源,受其影响,污水口下游的抗生素污染尤为严重.河水中氯霉素、土霉素和四环素在冬季的含量范围分别在2.1～19.0、ND～3.0、 0.8～6.8 μg·L^-1之间,夏季分别在0.2～1.3、ND～0.03、 0.2～0.3 μg·L^-1之间,金霉素只在冬季检出,含量范围在0.09～0.14 μg·L^-1之间;沉积物中4种抗生素在冬季的平均含量分别为147.6、 76.6、 99.2和1.6 μg·kg^-1;在夏季分别为195.8、 89.1、 34.4和9.0 μg·kg^-1.数据表明,河水中抗生素的含量受河水流量及来源特点的影响很大,冬季河水中抗生素的含量明显高于夏季;沉积物中抗生素的季节变化不明显.     

基于决策树的城市黑臭水体遥感分级

水体黑臭程度遥感监测是了解城市水质现状和综合评价城市水环境治理效果的重要手段.以南京、常州、无锡和扬州为研究区,共采集171个样点,同步测量水质参数和光学参数,分析黑臭水体与一般水体的水色和光学特征,构建决策树模型进行重度黑臭水体、轻度黑臭水体和非黑臭水体（记为一般水体）识别.结果表明：①根据色度可将水体分为1~6类水体,其中,类型1~4为黑臭水体,分别为灰黑色、深灰色、灰色和浅灰色水体,类型5和类型6水体为一般水体,分别为绿色系和黄色系水体;②类型1水体的非色素颗粒物和有色可溶性有机物含量高,但色素颗粒物的吸收并不占主导,类型2和5水体的吸收以色素颗粒物吸收占主导,类型3、4和6水体的吸收以非色素颗粒物吸收占主导;③根据六类水体的反射光谱差异用黑臭水体差值指数（difference of black-odorous water index,DBWI）、三波段面积水体指数（green-red-nir area water index,G-R-NIR AWI）、绿光波段反射率和归一化黑臭水体指数（normalized difference black-odorous water index,NDBWI）构建的水体分类识别决策树,能够有效识别出重、轻度黑臭水体和一般水体;④将决策树模型应用于2019年4月9日扬州的PlanetScope影像上,并利用10个同步过境点进行验证,整体识别精度达到80.00%,K值达到0.67.通过水色分类后的城市水体分级模型方法,可推广应用于类似的水体,为黑臭水体监管提供技术方法.