Migratory crustaceans as biomonitors of metal pollution in their nursery areas. The Lesina lagoon (SE Italy) as a case study

Lichens collected in an unpolluted forest (background) in November 2004, transplanted at 41 sampling sites along the Madina-Tetteh Quarshie road, retrieved in February 2005 and analysed were found to contain higher concentrations of Manganese (Mn), Vanadium (V), lead, Cadmium (Cd), Chromium (Cr) and Nickel (Ni) than in the background area. Observations showed that of all the heavy metals studied, Manganese concentrations were highest at all sampling points with maximum values around a traffic light, an intersection and car fitting/mechanic workshops. Significant correlations were found between Ni and Mn, Mn and Cr and Mn and V. Statistical analysis also revealed a relatively even dispersion of the studied elements on the eastern side of the road than on the western side.     

Fecal source tracking by antibiotic resistance analysis on a watershed exhibiting low resistance

The ongoing development of microbial source tracking has made it possible to identify contamination sources with varying accuracy, depending on the method used. The purpose of this study was to test the efficiency of the antibiotic resistance analysis (ARA) method under low resistance by tracking the fecal sources at Turkey Creek, Oklahoma exhibiting this condition. The resistance patterns of 772 water-isolates, tested with nine antibiotics, were analyzed by discriminant analysis (DA) utilizing a five-source library containing 2250 isolates. The library passed various representativeness tests; however, two of the pulled-sample tests suggested insufficient sampling. The resubstitution test of the library individual sources showed significant isolate misclassification with an average rate of correct classification (ARCC) of 58%. These misclassifications were explained by low antibiotic resistance (Wilcoxon test P < 0.0001). Seasonal DA of stream E. coli isolates for the pooled sources human/livestock/deer indicated that in fall, the human source dominated (P < 0.0001) at a rate of 56%, and that human and livestock respective contributions in winter (35 and 39%), spring (43 and 40%), and summer (37 and 35%) were similar. Deer scored lower (17–28%) than human and livestock at every season. The DA was revised using results from a misclassification analysis to provide a perspective of the effect caused by low antibiotic resistance and a more realistic determination of the fecal source rates at Turkey Creek. The revision increased livestock rates by 13–14% (0.04 ≤ P ≤ 0.06), and decreased human and deer by 6–7%. Negative misclassification into livestock was significant (0.04 ≤ P ≤ 0.06). Low antibiotic resistance showed the greatest effect in this category.     

Nutrient dynamics between sediment and overlying water in the inner part of Izmir Bay, Eastern Aegean

In this article a concept is described in order to predict and map the occurrence of benthic communities within and near the German Exclusive Economic Zone (EEZ) of the North Sea. The approach consists of two work steps: (1) geostatistical analysis of abiotic measurement data and (2) calculation of benthic provinces by means of Classification and Regression Trees (CART) and GIS-techniques. From bottom water measurements on salinity, temperature, silicate and nutrients as well as from punctual data on grain size ranges (0–20, 20–63, 63–2,000 μ) raster maps were calculated by use of geostatistical methods. At first the autocorrelation structure was examined and modelled with help of variogram analysis. The resulting variogram models were then used to calculate raster maps by applying ordinary kriging procedures. After intersecting these raster maps with punctual data on eight benthic communities a decision tree was derived to predict the occurrence of these communities within the study area. Since such a CART tree corresponds to a hierarchically ordered set of decision rules it was applied to the geostatistically estimated raster data to predict benthic habitats within and near the EEZ.     

太湖蓝藻水华时空分布与预警监测响应的分析

选择2007和2008年200幅EOS/MODIS太湖蓝藻监测遥感影像,统计分析了梅梁湾、竺山湾宜兴段、贡湖湾、东太湖胥口湾和湖州方向湖体蓝藻水华爆发的空间和时间分布规律。并在得出全太湖蓝藻水华空间和时间分布规律的基础上,从环境监测部门蓝藻预警监测工作的实际出发,将蓝藻水华预警监测的响应划分为常规监测和应急监测,提出了具体的监测要求,为环太湖地区的相关部门更好地开展蓝藻预警监测工作提供了科学依据。     

南通市生态环境遥感监测及其动态变化研究

应用遥感技术对南通市生态环境进行监测,利用GIS技术对遥感监测结果进行解译与分析,同时对2006-2009年南通市生态环境时空动态变化进行了分析与评价.结果表明,南通市生态环境类型以耕地、水域和城乡、工矿、居民用地为主,未利用土地面积极小.其生态环境动态变化的主要特点是耕地面积普遍有小幅度减少,以城镇建设用地为主的城乡、工矿、居民用地增加较多,林地面积无明显变化;未利用土地面积有所减少,以盐碱地面积减少为主,减少的面积主要用于耕地和建设用地.     

基于主成分分析-多元线性回归的松花江水体中多环芳烃源解析

对松花江全流域14个监测断面的16种美国环保局优先控制的多环芳烃(PAHs)的主要来源及其贡献率应用主成分因子分析-多元线性回归模型(PCA-MLR)进行了来源解析。结果表明:松花江全流域为化石和石油燃料的复合PAHs污染,水体环境中PAHs首要污染源为化石燃料燃烧和交通污染,合计贡献率为63.1%,第二大污染源为工业和民用燃煤污染,合计贡献率为36.9%,沿江的石化、石油基地、大型焦化厂、电厂都是PAHs的主要来源。     

基于环境一号卫星的PM2.5浓度遥感反演研究

2014年12月29—30日,青岛市发生了一次空气污染过程,利用环境一号卫星CCD遥感影像数据和实测PM_(2.5)浓度数据,分析了该过程前后5 d的实测PM_(2.5)浓度与环境一号卫星CCD遥感影像的DN值之间的相关性,分别按日期和区域建立了反演模型。结果表明,按日期拟合反演模型相关系数为0.02~0.51,按区域拟合反演模型相关系数为0.01~0.80,总体来说,区域拟合结果较好。     

云南曲靖某煤化工基地及周边土壤中多环芳烃的检测研究

采用快速溶剂提取处理土壤样品,利用气相色谱质谱内标法测定土壤中多环芳烃,其检出限范围为2.01~3.68μg/kg,相对标准偏差为2.6%~14.7%,加标回收率为67.1%~119%。结果表明,此方法具有良好的灵敏度、准确度和精密度。本研究对11个不同采样点土壤进行了检测分析,并考察了土壤中多环芳烃的污染水平。     

基于环境卫星CCD数据的黄海浒苔遥感监测

2007年以来,黄海海域每年夏季都发生大面积浒苔绿藻潮,影响沿岸人民的生活、生产。考虑到其分布范围动则几千、上万平方公里,水面监测很难实施,尝试利用遥感技术对其分布和发展变化情况进行监测。基于中国自主产权的环境卫星(HJ1)数据,采用经典植被指数算法和人工辅助判读方法,对2013年黄海浒苔暴发全过程进行了连续遥感监测,分析了此次浒苔过程的分布和漂移路线。监测结果表明,浒苔绿藻潮首发于江苏盐城东部海域,由南向北逐渐漂移,面积逐渐扩大,结束于青岛东北部海域;该期间,浒苔最大覆盖面积达663.54平方公里。     

基于PSO-SVM算法的环境监测数据异常检测和缺失补全

针对环境监测数据异常和数据缺失问题,提出了基于支持向量机的粒子群优化数据异常检测和缺失补全算法。利用粒子群优化算法选取较优的支持向量机训练参数组合,以此建立非线性的支持向量机模型,并利用结果模型对测得的真实数据拟合预测。以宁夏回族自治区某污水处理厂的污染物测量数据作为实验数据,结果表明,利用该算法预测数据的准确率可达97.977%,检测异常数据准确度高,缺失数据补全正确。