TPH-contaminated Mexican Refinery Soil: Health Risk Assessment and the First Year of Changes

The soil of a coastal Mexican refinery is quite contaminated, especially by hydrocarbons, with detected concentrations up to 130000 mg kg(-1) as TPHs (total petroleum hydrocarbons). The main sources of contamination are pipelines, valves, and old storage tanks, besides the land disposal of untreated hydrocarbon sediments derived from the cleaning of storage tanks. A health risk assessment (HRA) was carried out in order to measure the risk hazard indexes and clean-up standards for the refinery soil. HRA suggested the following actions to be taken: benzene concentrations must be reduced in eight of the 16 studied refinery zones to 0.0074-0.0078 mg kg(-1). Also, vanadium concentration must be reduced in two zones up to a concentration of 100 mg kg(-1). In only one of all of the studied zones, benzo(a)pyrene concentration must be reduced to 0.1 mg kg(-1). After 1 yr, TPHs showed a diminution of about 52%. Even though TPHs concentrations were variable, during 1999 the average concentrations were as much as 15.5 times the goal concentration. For year 2000, TPHs concentrations were only 7.4-fold the proposed value. For the 1999-2000 period, PAHs (polycyclic aromatic hydrocarbons) concentrations decreased by 82%. Some PAHs with 2, 3, 4, and 5 aromatic rings were removed up to 100% values.     

Effects of Roadway Crossings on Leaf Litter Processing and Invertebrate Assemblages in Small Streams

The effects of the Maine Turnpike (Interstate 95) on leaf litter processing were examined in five first- and second-order coastal plain streams in southern Maine, U.S.A. Invertebrate assemblages and red maple leaf softening and loss rates were compared at 53 stations arrayed upstream and downstream of the turnpike. Litter softening rate was not affected by the roadway. Litter loss rate was significantly faster at downstream stations (-0.0024 degree-day(-1)) than at upstream stations or at stations nearest the roadway, which were not different from each other (-0.0022 degree-day(-1)). Litter softening and loss rates were more strongly related to physical and chemical habitat variables than to shredder assemblage characteristics. Significant among-stream differences were observed in most community structural metrics and in biomass of important shredder taxa, but effects of the roadway were rarely consistent among streams. This is attributed in part to habitat variation, which was greater among streams than within streams. This study suggests that while the presence of the Maine Turnpike may influence stream water quality and habitat structure, the relatively subtle effects of roadway runoff and associated habitat modifications on stream ecosystem processes are masked by within- and among-stream variability in litter processing and leaf pack invertebrate assemblage structure.     

Development of an ombrotrophic peat bog (low ash) reference material for the determination of elemental concentrations

Given the increasing interest in using peat bogs as archives of atmospheric metal deposition, the lack of validated sample preparation methods and suitable certified reference materials has hindered not only the quality assurance of the generated analytical data but also the interpretation and comparison of peat core metal profiles from different laboratories in the international community. Reference materials play an important role in the evaluation of the accuracy of analytical results and are essential parts of good laboratory practice. An ombrotrophic peat bog reference material has been developed by 14 laboratories from nine countries in an inter-laboratory comparison between February and October 2002. The material has been characterised for both acid-extractable and total concentrations of a range of elements, including Al, As, Ca, Cd, Cr, Cu, Fe, Hg, Mg, Mn, Na, Ni, P, Pb, Ti, V and Zn. The steps involved in the production of the reference material (i.e. collection and preparation, homogeneity and stability studies, and certification) are described in detail.     

Quantitative PCR analysis of house dust can reveal abnormal mold conditions

Indoor mold concentrations were measured in the dust of moldy homes (MH) and reference homes (RH) by quantitative PCR (QPCR) assays for 82 species or related groups of species (assay groups). About 70% of the species and groups were never or only rarely detected. The ratios (MH geometric mean : RH geometric mean) for 6 commonly detected species (Aspergillus ochraceus, A. penicillioides, A. unguis, A. versicolor, Eurotium group, and Cladosporium sphaerospermum) were >1 (Group I). Logistic regression analysis of the sum of the logs of the concentrations of Group I species resulted in a 95% probability for separating MH from RH. These results suggest that it may be possible to evaluate whether a home has an abnormal mold condition by quantifying a limited number of mold species in a dust sample. Also, four common species of Aspergillus were quantified by standard culturing procedures and their concentrations compared to QPCR results. Culturing underestimated the concentrations of these four species by 2 to 3 orders of magnitude compared to QPCR.     

Organochlorine Pesticide Residues in Wheat and Drinking Water Samples from Jaipur, Rajasthan, India

The organochlorine pesticide contamination in dietary sources has caused serious threat to the human progeny. The present study was therefore conducted to evaluate the pesticide contamination in wheat flour and drinking water from Jaipur City, Rajasthan, India using Gas Chromatograph. All the wheat and water samples were found to be contaminated with various organochlorine pesticide residues of DDT and its metabolites, HCH and its isomers, heptachlor and its expoxide and aldrin. The amount of pesticide detected in wheat flour was higher than the permissible limits prescribed by WHO/FAO. In drinking water only a few pesticides exceeded the permissible limits. Seasonal variations of pesticides residues were also observed during the study period.     

Selection and Calibration of Numerical Modeling in Flow and Water Quality

Numerical modeling is frequently used in coastal engineering research and application. One possible issue associated with using this method however, is that initial outcomes might differ from expectation. Modeling manipulation addresses this issue by changing the initial parameters which in turn affects the final output results. The advancement of modeling techniques in recent years has seen a gradual but steady accruement of knowledge regarding the relationship between input parameters and possible outputs, which has resulted in improved accuracy and efficiency methods. The advancements within this field have predominantly occurred on an individual basis and as such lack standardisation. An expert numerical modeler may use this knowledge subconsciously, yet may not know how to convey it to the model users. This paper has been written to introduce a systematic intelligent coding schema designed to organise current coastal engineering modeling manipulation knowledge into a standardised format. This system is relevant to the development of appropriate strategies for improved accuracy and efficiency as well as model modification to simulate specific real phenomena in prototype application cases.     

膜、柱串联固相萃取-高效液相色谱法测定水中多环芳烃(PAHs)的研究

建立了用膜-柱串联固相萃取(SPE)技术,甲醇和水作为流动相进行梯度洗脱,紫外和荧光检测器串联的高效液相色谱法(HPLC)分析水中EPA优先监控的16种PAHs的方法.     

How and why is Aquatic Quality Changing at Nahanni National Park Reserve, nwt, Canada?

Nahanni National Park Reserve is located at southwestern NWT-Yukon border. One of the first UNESCO World Heritage sites, Nahanni lies within Taiga Cordillera and Taiga Shield Ecozones. Base and precious metal mining occurred upstream of Nahanni prior to park establishment. Nahanni waters, sediments, fish, and caribou have naturally elevated metals levels. Baseline water, sediment and fish tissue quality data were collected and analyzed throughout Nahanni during 1988–91 and 1992–97. These two programs characterized how aquatic quality variables are naturally varying in space and time, affected by geology, stream flow, seasonality, and extreme meteorological and geological events. Possible anthropogenic causes of aquatic quality change were examined. Measured values were compared to existing Guidelines and site-specific objectives were established.     

A pilot project to detect and forecast harmful algal blooms in the northern Gulf of Mexico

More timely access to data and information on the initiation, evolution and effects of harmful algal blooms can reduce adverse impacts on valued natural resources and human health. To achieve this in the northern Gulf of Mexico, a pilot project was initiated to develop a user-driven, end-to-end (measurements to applications) observing system. A key strategy of the project is to coordinate existing state, federal and academic programs at an unprecedented level of collaboration and partnership. Resource managers charged with protection of public health and aquatic resources require immediate notice of algal events and a forecast of when, where and what adverse effects will likely occur. Further, managers require integrated analyses and interpretations, rather than raw data, to make effective decisions. Consequently, a functional observing system must collect and transform diverse measurements into usable forecasts. Data needed to support development of forecasts will include such properties as sea surface temperature, winds, currents and waves; precipitation and freshwater flows with related discharges of sediment and nutrients; salinity, dissolved oxygen, and chlorophyll concentrations (in vivo fluorescence); and remotely-sensed spatial images of sea surface chlorophyll concentrations. These data will be provided via a mixture of discrete and autonomous in situ sensing with near real-time data telemetry, and remote sensing from space (SeaWiFS), aircraft (hyperspectral imagery) or land (high-frequency radar). With calibration across these platforms, the project will ultimately provide a 4-dimensional visualization of harmful algae events in a time frame suitable to resource managers.     

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.