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.     

北京市昌平区大气环境PM10中多环芳烃的含量及变化趋势

通过采集了2004～2006年北京市昌平区四个季节中大气PM10样品,采用超声抽提方法,使用GC/MS分析了该区PAHs含量和组成.结果显示,三年中四个季度的18种PAHs总量范围分别为21.64～656.39ng/m3、31.94～164.33ng/m3和7.294～209.3ng/m3,其中致癌性极强的苯并[a]芘含量范围为2.69～36.95 ng/m3、1.44～6.6ng/m3和0.256～8.625ng/m1,其变化趋势与PAHs总量有较好的相关性.PAHs的浓度是冬季＞秋季＞夏季＞春季,这与夏季时雨水冲刷和阳光照射强度大导致PAHs光解,冬季时燃煤排放大等影响因素有关.文章还使用多种方法判断昌平区大气PM10中的PAHs主要来源于燃煤和汽车尾气,其它污染源贡献较小.     

集合数值预报系统在上海市空气质量预测预报中的应用研究

介绍了上海市世博环境空气质量集合数值预报系统的框架,分析了各模式在上海市空气质量预报及排放源污染贡献测算中的应用情况,提出开发多种预报手段、实施集合预报是提高城市空气质量预报准确率的发展方向。     

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。     

二维平流扩散模型下的集合卡尔曼滤波模拟同化研究

为提高环境数值预报水平,构建了一个针对污染物扩散的模拟数据同化系统。采用集合卡尔曼滤波方法对二维平流扩散模型的状态变量进行了实时校正,实现污染物浓度的实时模拟预报,完成了敏感性实验中集合数目变化、观测方差变化和同化窗口长度变化研究。比较考察观测点位置与污染源距离不同时的预报效果,探讨了优化条件下的同化策略,提出一种根据距离远近动态调节卡尔曼增益权重的方法。在集合数目较小时,可降低计算代价,得到优化的同化效果。     

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。     

固相萃取-GC/MS法测定水中持久性有机污染物

采用Envi-18固相萃取小柱富集水样,用正己烷/丙酮混合溶剂洗脱水样中42种POPs化合物,再用气质联用法测定。通过优化仪器监测条件,使目标物在0.250 mg/L~5.00 mg/L范围内线性良好。方法检出限为50.9 ng/L~11 640 ng/L,实际水样加标回收率为57.1%~129%,测定结果的RSD为0.1%~13.8%。将该方法用于测定浙江省东苕溪流域的45份水样,结果滴滴涕、六六六等有机氯农药的检出率较高,其他POPs的检出率较低。