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.     

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。     

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。     

集中式饮用水源地邻苯二甲酸酯类物质分布特征与健康风险评估

以某地区7个集中式饮用水源地为研究对象,采用固相萃取气相色谱-质谱法(SPE-GC-MS)对水体中16种邻苯二甲酸酯的分布特征和溯源进行了研究,并利用健康风险评估模型对水体PAEs进行了健康风险评价。结果表明:邻苯二甲酸二正丁酯、邻苯二甲酸二(2-乙基己基)酯、邻苯二甲酸二异丁酯和邻苯二甲酸二正辛酯在所有PAEs同系物中含量丰富,而所有样品均无邻苯二甲酸二(2-甲氧基)乙酯和邻苯二甲酸二戊酯的检出;二水厂和亨达水务断面Σ_(16)PAEs浓度最高,四水厂和五水厂断面Σ_(16)PAEs浓度最低;水体12种PAEs共提出3个主成分,揭示了91%的影响因子;层次聚类分析表明:7个采样断面聚为2类,分别代表了内河和长江水体。水体中PAEs的致癌风险值和非致癌风险值均远低于参考值,说明研究水体PAEs不会对居民构成致癌风险或其他明显的健康风险,但需加强该地区PAEs使用的规范与监管,强化末端处理,以规避风险。     

退浆废水中聚乙烯醇的分析检测研究

建立了紫外可见分光光度法检测退浆废水中聚乙烯醇(PVA)含量的方法。以硼酸为介质,碘化钾—碘溶液为显色剂,检测波长为680 nm,测定退浆废水中PVA含量的线性方程为Y=0.018 8X+0.003 2,相关系数R2=0.999 3,平均加标回收率为97.6%;方法精密度(RSD=1.5%)、重现性(RSD=1.8%)均较好。该方法操作简单、准确度高、测定快速,适用于测定退浆废水中PVA的含量。     

氨基化有机-无机杂化整体柱分离富集环境水中五价砷

将四乙氧基硅烷(TEOS)和N-(β-氨乙基)-γ-氨丙基三乙氧基硅烷(AEAPTES)作为共聚前体,采用在毛细管内原位聚合的方式制备得到氨基功能化的有机-无机杂化整体柱,将其作为针式固相微萃取(SPME)介质,建立了现场分离富集环境水中As(V)的SPME方法,优化了有机-无机杂化整体柱富集As(V)的实验条件,并研究了整体柱对As(V)的吸附/洗脱性能和富集能力,实现了环境水样中As(V)的高效、快速、高选择性检测。