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.     

Modelling vegetation greenness responses to climate variability in a Mediterranean terrestrial ecosystem

Accurate knowledge of the quality and environmental impact of the highway runoff in Pear River Delta, South China is required to assess this important non-point pollution source. This paper presents the quality characterization and environmental impact assessment of rainfall runoff from highways in urban and rural area of Guangzhou, the largest city of Pear River Delta over 1 year’s investigation. Multiple regression and Pearson correlation analysis were used to determine influence of the rainfall characteristics on water quality and correlations among the constituents in highway runoff. The results and analysis indicates that the runoff water is nearly neutral with low biodegradability. Oil and grease (O&G), suspended solids (SS) and heavy metals are the dominant pollutants in contrast to the low level of nutrient constituents in runoff. Quality of highway runoff at rural site is better than that of at urban site for most constituents. Depth and antecedent dry period are the main rainfall factors influencing quality of highway runoff. The correlation patterns among constituents in highway runoff at urban site are consistent with their dominant phases in water. Strong correlations (r ≥ 0.80) are found among chemical oxygen demand (COD), total phosphorus, Cu and Zn as well as conductivity, nitrate nitrogen and total nitrogen. O&G, COD, SS and Pb in highway runoff at urban site substantially exceed their concentrations in receiving water of Pear River. The soil directly discharged by highway runoff at rural site has contaminated seriously by heavy metals in surface layer accompanying with pH conversion from original acidic to alkaline at present.     

Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

The interaction effects of abiotic processes in the production of phytoplankton in a coastal marine region off Cochin are evaluated using multiple regression models. The study shows that chlorophyll production is not limited by nutrients, but their physiological regulations (responses to nutrients, pH, temperature and salinity) are mainly responsible for the increased biological production. The model explaining 77% of variability for chlorophyll a production is indicative of preconditioning of the coastal waters. The phytoplankton production is found to be sensitive to the environment, which varies seasonally. Further, the study suggests that supply of organic matter and grazing of zooplankton (not included) would improve the model efficiency. Despite this, the good agreement in the computed and measured chlorophyll a values shows that step-up multiple regression model is a useful tool to understand the influence of environmental variables on the production of phytoplankton in these coastal waters.     

粪大肠菌群多管发酵测定法的改进研究

对地表水和废水中粪大肠菌群目前通用的多管发酵检测方法进行优化。将水样接种至乳糖蛋白胨培养液后,在44.5℃±0.5℃培养箱中培养24h记录结果,并与优化前的方法进行对比。结果表明,改进后的方法在时间上比改进前节省了约24~48小时,但两者在结果上无统计学意义的差别。多管发酵改进法可以用于地表水和废水中粪大肠菌群的检测。     

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。     

Plant communities in relation to flooding and soil contamination in a lowland Rhine River floodplain

Using canonical correspondence analysis (CCA), relationships were investigated between plant species composition and flooding characteristics, heavy metal contamination and soil properties in a lowland floodplain of the Rhine River. Floodplain elevation and yearly average flooding duration turned out to be more important for explaining variation in plant species composition than soil heavy metal contamination. Nevertheless, plant species richness and diversity showed a significant decrease with the level of contamination. As single heavy metal concentrations seemed mostly too low for causing phytotoxic effects in plants, this trend is possibly explained by additive effects of multiple contaminants or by the concomitant influences of contamination and non-chemical stressors like flooding. These results suggest that impacts of soil contamination on plants in floodplains could be larger than expected from mere soil concentrations. In general, these findings emphasize the relevance of analyzing effects of toxic substances in concert with the effects of other relevant stressors.     

Environmental conditions enhance toxicant effects in larvae of the ground beetle Pterostichus oblongopunctatus (Coleoptera: Carabidae)

The wide geographical distribution of ground beetles Pterostichus oblongopunctatus makes them very likely to be exposed to several environmental stressors at the same time. These could include both climatic stress and exposure to chemicals. Our previous studies demonstrated that the combined effect of nickel (Ni) and chlorpyrifos (CHP) was temperature (T)-dependent in adult P. oblongopunctatus. Frequently the different developmental stages of an organism are differently sensitive to single stressors, and for a number of reasons, such as differences in exposure routes, their interactions may also take different forms. Because of this, we studied the effects of the same factors on the beetle larvae. The results showed that all factors, as well as their interactions, influenced larvae survival. The synergistic effect of Ni and CPF was temperature-dependent and the effect of Ni × T interaction on the proportion of emerged imagines indicated stronger toxicity of Ni at 25 °C than at 10 °C.     

Inner City regeneration: perspectives on the G.E.A.R. Project

Government responses to the problems of the inner city currently focus on comprehensive programmes of positive discrimination. The first and most extensive is the G.E.A.R. project in Glasgow. Regular monitoring of the effectiveness of such large‐scale initiatives is essential. If the physical and social aims of such policies are to be realised, assessments must utilise both objective and subjective indicators.     

Do mountain log skinks (<Emphasis Type="Italic">Pseudemoia entrecasteauxii</Emphasis>) modify their behaviour in the presence of two predators?

Prey often adopt antipredator strategies to reduce the likelihood of predation. In the presence of predators, prey may use antipredator strategies that are effective against a single predator (specific) or that are effective against several predators (nonspecific). Most studies have been confined to single predator environments although prey are often faced with multiple predators. When more than one predator is present, specific antipredator behaviours can conflict and avoidance of one predator may increase vulnerability to another. To test how prey cope with this dilemma, I recorded the behaviours of lizards responding to the nonlethal cues of a bird and snake presented singly and simultaneously. Lizards use specific and conflicting antipredator tactics when confronted with each predator, as evidenced by refuge use. However, when both predators were present, lizards refuge use was the same as in the predator-free environment, indicating that they abandoned refuge use as a primary mechanism for predator avoidance. In the presence of both predators, they reduced their overall movement and time spent thermoregulating. This shift in behaviour may represent a compromise to minimize overall risk, following a change in predator exposure. This provides evidence of plasticity in lizard antipredator behaviour and shows that prey responses to two predators cannot be accurately predicted from what is observed when only one predator is present.Communicated by W. Cooper     

Predicting mass rapid transit noise levels on an elevated station

This study developed a noise prediction model for elevated mass rapid transit (MRT) platforms. Relevant physical and operational parameters (e.g. cruise speed, acceleration and deceleration rates for trains, building fa?ade setbacks and so on) were collected from the Bangkok mass transit system (BTS), the first elevated MRT system operated in Bangkok, Thailand. The equivalent continuous sound pressure levels (L(Aeq)) were collected from both sides of the MRT stations at the center of each platform. The relevant parameters were collected on both platforms and ground level, on both sides of MRT stations. These parameters were statistically tested to determine their correlation with MRT noise. The final model was built from highly correlated parameters using multiple regression analysis with a stepwise regression technique. Statistical evaluation showed a high degree of goodness-of-fit test for the model to the observed data. Therefore, it can be efficiently used for the projection of MRT noise in the affected areas.