Characterization of bacterial contaminants in the air of a duck hatchery by cultivation based and molecular methods

Today's large-scale poultry production is often accompanied by high concentrations of airborne microorganisms at working places. However, the microbial communities in those bioaerosols are rarely characterised. In this study, we investigated the bacterial population in bioaerosols from a duck hatchery by both cultivation based and molecular methods and compared the results. Depending on used media, concentrations of airborne culturable bacteria varied between 6 × 10(1) and 7 × 10(6) CFU per m(3) air. The corresponding total cell count of DAPI stained cells was 2 × 10(7) cells per m(3) air. 16S rRNA gene analyses of bacterial isolates and clone libraries revealed a low species richness in hatcheries air, respectively. More than 50% of bacterial isolates were phylogenetically most closely related to bacterial species of the risk group 2 (German TRBA). The sequence composition in clone libraries supported the result of cultivation based approaches, whereby sequences assigned to Staphylococcus, Acinetobacter and Enterococcus are the most common. The high concentration of airborne bacteria which are most closely related to species of potential health risk requires further detailed investigations for these bacterial species.     

A new attachment device for deployment of monitoring equipment in estuaries and other high-energy environments

Biomonitoring is an important component of estuarine research and monitoring programs because living organisms integrate biological, chemical, and physical conditions over time. The deployment of biomonitoring devices in ecosystems that are subject to changes in water level and flow can be very challenging. This paper describes a new device, which facilitates such applications such as the deployment of periphytometers. The device is designed to encircle posts, poles, or pilings, such as channel markers common in many waterways. This device has been evaluated and approved for use by the US Coast Guard, needed for attachment to navigational aids. It allows attachment of monitoring devices requiring in situ deployment at fixed water depths in systems with dynamic water levels or velocities while minimizing the potential for shading, damage, theft, or poor long-term performance.     

Water quality trends in the Delaware River Basin (USA) from 1980 to 2005

In 1940, the tidal Delaware River was ??one of the most grossly polluted areas in the United States.?? During the 1950s, water quality was so poor along the river at Philadelphia that zero oxygen levels prevented migration of American shad leading to near extirpation of the species. Since then, water quality in the Delaware Basin has improved with implementation of the 1961 Delaware River Basin Compact and 1970s Federal Clean Water Act Amendments. At 15 gages along the Delaware River and major tributaries between 1980 and 2005, water quality for dissolved oxygen, phosphorus, nitrogen, and sediment improved at 39%, remained constant at 51%, and degraded at 10% of the stations. Since 1980, improved water-quality stations outnumbered degraded stations by a 4 to 1 margin. Water quality remains good in the nontidal river above Trenton and, while improved, remains fair to poor for phosphorus and nitrogen in the tidal estuary near Philadelphia and in the Lehigh and Schuylkill tributaries. Water quality is good in heavily forested watersheds (>50%) and poor in highly cultivated watersheds. Water quality recovery in the Delaware Basin is coincident with implementation of environmental laws enacted in the 1960s and 1970s and is congruent with return of striped bass, shad, blue crab, and bald eagle populations.     

Monitoring fluvial water chemistry for trend detection: hydrological variability masks trends in datasets covering fewer than 12 years

This paper sub-samples four 35 year water quality time series to consider the potential influence of short-term hydrological variability on process inference derived from short-term monitoring data. The data comprise two time series for nitrate (NO(3)-N) and two for DOC (using water colour as a surrogate). The four catchments were selected not only because of their long records, but also because the four catchments are very different: upland and lowland, agricultural and non-agricultural. Multiple linear regression is used to identify the trend and effects of rainfall and hydrological 'memory effects' over the full 35 years, and then a moving-window technique is used to subsample the series, using window widths of between 6 and 20 years. The results suggest that analyses of periods between six and eleven years are more influenced by local hydrological variability and therefore provide misleading results about long-term trends, whereas periods of longer than twelve years tend to be more representative of underlying system behaviour. This is significant: if such methods for analysing monitoring data were used to validate changes in catchment management, a monitoring period of less than 12 years might be insufficient to demonstrate change in the underlying system.     

Solid-phase extraction using hierarchical organosilicates for enhanced detection of nitroenergetic targets

A novel porous organosilicate material was evaluated for application as a solid phase extraction sorbent for preconcentration of nitroenergetic targets from aqueous solution prior to HPLC analysis. The performance of the sorbent in spiked deionized water, groundwater, and surface water was evaluated. Targets considered included 2,4,6-trinitrotoluene, 2,4-dinitrotoluene, RDX, HMX, and nitroglycerin. The sorbent was shown to provide improved performance over Sep-Pak RDX. The impact of complex matrices on target preconcentration by the sorbent was also found to be less dramatic than that observed for LiChrolut EN. The impact of changes in pH on target preconcentration was considered. Aqueous soil extracts generated from samples collected at sites of ordnance testing were also used to evaluate the materials. The results presented here demonstrate the potential of this novel sorbent for application as a solid phase extraction material for the preconcentration of nitroenergetic targets from aqueous solutions.     

Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Trihalomethanes in Drinking Water of Greater Québec Region (Canada): Occurrence, Variations and Modelling

The levels of trihalomethanes (THMs) – the main species of by-product from water chlorination – were monitored in thedistribution systems of the five major drinking water utilitiesof the greater area of Québec City in order to investigate andmodel their occurrence on a spatial and seasonal basis. Data forTHMs and other water quality and operational parametersassociated with their formation were generated through a 16 monthsampling program involving several sites representing variablewater residence times, from the plant to the system extremity.The results demonstrate that the differences in measured THMlevels between the five utilities are mainly due to the variablequality of raw waters, the type of water treatment process beingused and the type and levels of applied disinfectant. Dependingon the utility, average THM levels were from 1.3 to 2.5 timeshigher in the system extremities than in the water leaving thetreatment plant. Also, average levels of THMs measured in summerat the distribution system extremities were, depending on theutility, from 2.5 to 5 times higher than the average levelsmeasured in winter. The seasonal differences were found to besignificantly greater than those observed by others in waterutilities in the United States and Europe and are explained inlarge part by the considerable changes, over the year, in thequality and temperature of surface waters in Southern Québec. Forthe five utilities under study, multivariate regression modelswere developed in order to predict spatial and seasonalvariations of THMs. Both residual chlorine demand and temperaturewere found to be better, statistically, as predictors for THMoccurrence. The usefulness of the developed models for routineand long term water quality management, as well as for assessmentof human exposure to THMs, are also discussed.     

Sharing the geo-Referenced Results of Climate Change Impact Research

The Prairie Adaptation Research Collaborative (PARC) has implemented an Internet Map Server (IMS) at the PARC web site (www.parc.ca) to 1) disseminate the geo-referenced results of PARC sponsored research on climate change impacts and adaptation, and 2) address data, information and knowledge management within the PARC network of researchers and partners. PARC facilitates interdisciplinary research on adaptation to the impacts of climate change in the Canadian Prairie Provinces. The web site is intended as a platform for sharing information and encouraging discussion of climate change impacts and adaptation. The IMS enables scientists and stakeholders to apply simple climate change scenarios to geo-referenced biophysical and social data, and dynamically create maps that display the geographic distribution of potential impacts of climate change. With a limited capacity for spatial analysis, most geo-processing and the climate impact modeling is done offline within a GIS environment. The IMS will serve the output from climate impact models, such that the model results can be customized by the web site user and be most readily applied to the planning and analysis of adaptation strategies.     

Application of the benthic index of biotic integrity to environmental monitoring in Chesapeake Bay

The Chesapeake Bay benthic index of biotic integrity (B-IBI) was developed to assess benthic community health and environmental quality in Chesapeake Bay. The B-IBI provides Chesapeake Bay monitoring programs with a uniform tool with which to characterize bay-wide benthic community condition and assess the health of the Bay. A probability-based design permits unbiased annual estimates of areal degradation within the Chesapeake Bay and its tributaries with quantifiable precision. However, of greatest interest to managers is the identification of problem areas most in need of restoration. Here we apply the B-IBI to benthic data collected in the Bay since 1994 to assess benthic community degradation by Chesapeake Bay Program segment and water depth. We used a new B-IBI classification system that improves the reliability of the estimates of degradation. Estimates were produced for 67 Chesapeake Bay Program segments. Greatest degradation was found in areas that are known to experience hypoxia or show toxic contamination, such as the mesohaline portion of the Potomac River, the Patapsco River, and the Maryland mainstem. Logistic regression models revealed increased probability of degraded benthos with depth for the lower Potomac River, Patapsco River, Nanticoke River, lower York River, and the Maryland mainstem. Our assessment of degradation by segment and water depth provided greater resolution of relative condition than previously available, and helped define the extent of degradation in Chesapeake Bay.