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.     

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。     

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。     

溶剂效应对有机氯农药测定的影响研究

采用气相色谱-质谱法(GC-MS)研究了不同溶剂对有机氯农药测定的影响。结果表明,有机氯农药在不同溶剂中的GC-MS响应值是有差异的,但保留时间不变,且随着有机氯农药质量浓度的增加,这种溶剂效应对大部分有机氯农药的作用减弱。经分析溶剂的极性对有机氯农药测定有显著的影响。     

江苏省典型汽车涂装企业VOCs排放特征与污染控制技术

利用物料衡算和源排放测试对江苏省典型汽车涂装企业VOCs排放特征进行研究,并提出最佳治理技术。结果表明,大客车单位涂装面积VOCs排放量达到300 g/m2以上,小轿车为40~60 g/m2。苯系物是VOCs排放的重要组分,最高占比为33.2%~64.6%。乙酸丁酯、异丙醇、丁醇等醇酯类物质近年来广泛用于代替苯系物溶剂,其排放占比为29.6%~61.2%。汽车涂装行业最佳治理技术包括采用3C1B、水性免中涂等先进涂装工艺,用粉末涂料、水性涂料和高固体成分涂料等代替溶剂型涂料,从源头控制排放。采用干式漆雾分离技术、转轮浓缩吸附-蓄热式焚烧技术等先进尾气治理技术,VOCs去除率可达99%以上。     

Using Multi-Scale Sampling and Spatial Cross-Correlation to Investigate Patterns of Plant Species Richness

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, I _YZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m²) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m² subplots within each of the 1000 m² plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.