An Approach to Reaeration Coefficient Modeling in Local Surface Water Quality Monitoring

Reaeration coefficient (k ₂) for River Atuwara, Ogun State, Nigeria was calculated from dissolved oxygen and biochemical oxygen demand data collected over period of 3 months covering the two prevailing climatic seasons in the country. Both the Akaike and Bayesian information criteria were used in the selection and analysis of ten models to identify the most suitable reaeration coefficient (k ₂) model for Atuwara River. Models that passed the confidence limit were subjected to model evaluation using measures of agreement between observed and predicted data such as percent bias, Nash–Sutcliffe efficiency, and root mean square observation standard deviation ratio. The used approach yield better results than empirical models developed for local conditions while it is also useful in conserving scarce resources.     

Vegetation response to hydrologic and geomorphic factors in an arid region of the Baja California Peninsula

Remotely sensed imageries were used to analyze the response of desert vegetation to physiographic factors and accumulated precipitation in drier and wetter years within a region of >16,500 km² sampled with 5,000 random pixels of 30 m. Vegetation development was indexed by the annual maximum values for greenness (SAVI) and canopy water content (NDII). Precipitation was interpolated from the 0.25° grid of the Tropical Rainfall Measurement Mission satellite-based estimates, showing a regional average of ～55 mm in the wetter year. The vegetation indices were only weakly related to total precipitation, often in a negative sense. Terrain factors that most often affected the vegetation indices, in multiple regression models, were Topographic Wetness Index, elevation, and slope gradient; these often had different signs for SAVI and for NDII. Models for NDII on intrusive igneous rocks gave better results than on extrusive igneous rocks. The strongest patterns in vegetation development were the contrast among Pacific coast, Cordillera, and Gulf coast subregions and the generally stronger results for NDII than SAVI.     

Impact of rapid urbanisation and industrialisation on river sediment metal contamination

This study aimed at evidencing contaminant inputs from a rapidly growing population and the accompanying anthropogenic activities to river sediments. The Fez metropolitan area and its impacts on the Sebou's sediments (the main Moroccan river) were chosen as a case study. The Fez agglomeration is surrounded by the river Fez, receiving the wastewaters of this developing city and then flowing into the Sebou. The sediment cores from the Fez and Sebou Rivers were extracted and analysed for major elements, butyltins and toxic metals. Normalised enrichment factors and geoaccumulation index were calculated. Toxicity risk was assessed by two sets of sediment quality guideline (SQG) indices. A moderate level of contamination by butyltins was observed, with monobutyltin being the dominant species across all sites and depths. The lowest level of metal pollution was identified in the Sebou's sediments in upstream of Fez city, whilst the Fez' sediments were heavily polluted and exhibited bottom-up accumulation trends, which is a clear signature of recent inputs from the untreated wastewaters of Fez city. Consequently, the sediments of Fez and Sebou at the downstream of the confluence were found to be potentially toxic, according to the SQG levels. This finding is concerned with aquatic organisms, as well as to the riverside population, which is certainly exposed to these pollutants through the daily use of water. This study suggests that although Morocco has adopted environmental regulations aiming at restricting pollutant discharges into the natural ecosystems, such regulations are neither well respected by the main polluters nor efficiently enforced by the authorities.     

Lognormality of trace contaminant concentrations in sewage effluents

It is important to understand the statistical distribution of monitoring data for them to be of value in determining the parameters of environmental models. No such distributional information has been available for many trace contaminants in sewage effluents. This paper applies the data of a major UK sewage works’ effluent monitoring programme to determine the validity of the common assumption that data are lognormally distributed. Effluent quality was monitored at 162 wastewater treatment works over 1 year, generating over 3,000 results for each of over 40 substances, including metals, trace organic substances and pharmaceuticals. It is demonstrated that the lognormal assumption is clearly justified for the great majority of substances in the spatial case—for annual average effluent concentrations across different treatment works. In the site-specific, temporal case—for individual determinations of concentration at a single site over an annual period—lognormality is generally supported but not demonstrated so unequivocally for all site/substance combinations. The principal source of uncertainty was lack of sufficient numbers of observations reported to adequately low reporting limits.     

Toxicodynamic modeling of 137Cs to estimate white-tailed deer background levels for the Department of Energy's Savannah River Site

The U.S. Department of Energy's (USDOE) Savannah River Site (SRS) is a former nuclear weapon material production and current research facility adjacent to the Savannah River in South Carolina, USA. The purpose of this study was to determine the background radiocesium (¹³⁷Cs) body burden (e.g., from global fallout) for white-tailed deer (Odocoileus virginianus) inhabiting the SRS. To differentiate what the background burden is for the SRS versus ¹³⁷Cs obtained from SRS nuclear activities, data were analyzed spatially, temporally and compared to other off-site hunting areas near the SRS. The specific objectives of this study were: to compare SRS and offsite deer herds based on time and space; to interpret comparisons based on how data were collected as well as the effect of environmental and anthropogenic influences; to determine what the ecological half-life/decay rate is for ¹³⁷Cs in the SRS deer herd; and to give a recommendation to what should be considered the background ¹³⁷Cs level in the SRS deer herd. Based on the available information and analyses, it is recommended that the determination of what is considered background for the SRS deer herd be derived from data collected from the SRS deer herd itself and not offsite collections for a variety of reasons. Offsite data show extreme variability most likely due to environmental factors such as soil type and land-use patterns (e.g., forest, agriculture, residential activities). This can be seen from results where samples from offsite military bases (Fort Jackson and Fort Stewart) without anthropogenic ¹³⁷Cs sources were much higher than both the SRS and a nearby (Sandhills) study site. Moreover, deer from private hunting grounds have the potential to be baited with corn, thus artificially lowering their ¹³⁷Cs body burdens compared to other free-ranging deer. Additionally, sample size for offsite collections were not robust enough to calculate a temporal decay curve with an upper confidence level to determine if the herds are following predicted radioactive decay rates like the SRS or if the variability is due to those points described above. Using mean yearly values, the ecological half-life for ¹³⁷Cs body burdens for SRS white-tailed deer was determined to be 28.79 years—very close to the 30.2 years physical half-life.     

Feature extraction using Hough transform for solid waste bin level detection and classification

This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image’s gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system.     

Biomonitoring of heavy metals (Cd,Hg, and Pb) and metalloid (As) with the Portuguese common buzzard (Buteo buteo)

The accumulation of heavy metals in the environment may have a wide range of health effects on animals and humans. Thus, in this study, the concentrations of arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg) in the blood and tissues (liver and kidney) of Portuguese common buzzards (Buteo buteo) were determined by inductively coupled plasma-mass spectrometer (ICP-MS) in order to monitor environmental pollution to these elements. In general, Hg and As were the elements which appeared in the highest and lowest concentrations, respectively. A highest percentage of non-detected concentration was found for blood Cd (94.6 %) but, in turn, it was the only metal that was detected in all kidney samples. The kidney was the analyzed sample which showed the highest concentrations of each element evaluated. Statistically, significant differences among blood, liver, and kidney samples were observed for As and Cd (P?相似文献