Physical,chemical, and mechanical properties of landfilled waste from Campina Grande,Brazil

Environmental Science and Pollution Research - The properties of municipal solid waste (MSW) in landfills vary considerably, depending on the waste’s composition, time, and density. This...     

Effects of exposure to the UV-filter 4-MBC during Solea senegalensis metamorphosis

Environmental Science and Pollution Research - Many personal care products integrate UV-filters, such as 4-methylbenzylidene camphor (4-MBC), a compound frequently detected in aquatic habitats,...     

Are rapid population estimates accurate? A field trial of two different assessment methods

Emergencies resulting in large-scale displacement often lead to populations resettling in areas where basic health services and sanitation are unavailable. To plan relief-related activities quickly, rapid population size estimates are needed. The currently recommended Quadrat method estimates total population by extrapolating the average population size living in square blocks of known area to the total site surface. An alternative approach, the T-Square, provides a population estimate based on analysis of the spatial distribution of housing units taken throughout a site. We field tested both methods and validated the results against a census in Esturro Bairro, Beira, Mozambique. Compared to the census (population: 9,479), the T-Square yielded a better population estimate (9,523) than the Quadrat method (7,681; 95% confidence interval: 6,160-9,201), but was more difficult for field survey teams to implement. Although applicable only to similar sites, several general conclusions can be drawn for emergency planning.     

The link between pharmaceuticals and cyanobacteria: a review regarding ecotoxicological,ecological, and sanitary aspects

Environmental Science and Pollution Research - Cyanobacteria are important for ecosystem functioning, but eutrophication may affect the surrounding biome by losing ecosystem services and/or through...     

Transport and acclimation conditions for the use of an estuarine fish (Pomatoschistus microps) in ecotoxicity bioassays: effects on enzymatic biomarkers

Acclimation of organisms for ecotoxicity testing is in general processed according to Organisation for Economic Co-operation and Development (OECD) and/or Environmental Protection Agency (EPA) guidelines, under controlled conditions. However, when organisms are collected in the field, their capture, transport and adaptation to laboratory conditions are factors of stress. In their natural habitat, estuarine fish are exposed to considerable fluctuations of environmental variables, while in laboratory they are acclimated to constant conditions and this can be per se a factor of stress that may influence biomarker responses. Therefore, it is important to investigate the effects of these procedures on estuarine fish performance before using them as test organisms in ecotoxicity bioassays. The goal of the present study was to investigate the effects of transporting the common goby, Pomatoschistus microps from the field (natural populations) to the laboratory and of its acclimation to laboratory conditions on the enzymes acetylcholinesterase (AChE), lactate dehydrogenase (LDH) and glutathione S-transferases (GSTs). Fish were collected in a reference site of the Minho River estuary (NW of Portugal) and the activities of the biomarkers were monitored before and after the transport of organisms to the laboratory and during the acclimation period (at 5, 10 and 15 days). The activities of all the enzymes indicated that capture and transport conditions had no effects on enzymatic activities. Furthermore, AChE, LDH and GST presented higher activities at the end of the acclimation period than at beginning, suggesting a physiological adaptation to laboratory conditions. This adaptation should be taken into consideration in the experimental design to avoid bias in the interpretation of effects of xenobiotics on biomarkers.     

Occurrence of tar balls on the beaches of Fernando de Noronha Island,South Equatorial Atlantic

This work reports on the widespread occurrence of tar balls on a pebble beach of Sueste Bay on Fernando de Noronha Island, a Brazilian national marine park and a preserve in the South Equatorial Atlantic. Environmental regulations preclude regular visitors to the Sueste Bay beach, and the bay is a pristine area without any possible or potential sources of petroleum in the coastal zone. In this work, these tar balls were observed for the first time as they occurred as envelopes around beach pebbles. They are black in color, very hard, have a shell and coral fragment armor, and range in average size from 2 to 6 cm. The shape of the majority of the tar balls is spherical, but some can also be flattened ellipsoids. The polycyclic aromatic hydrocarbon analyses of the collected samples revealed the characteristics of a strongly weathered material, where only the most persistent compounds were detected: chrysene, benzo(b,k)fluoranthene, dibenzo(a,h)antracene and benzo(a)pyrene.     

Investigating the complex relationship between in situ Southern Ocean pCO_{2} and its ocean physics and biogeochemical drivers using a nonparametric regression approach

The objective in this paper is to investigate the use of a non-parametric approach to model the relationship between oceanic carbon dioxide \((pCO_2)\) and a range of ocean physics and biogeochemical in situ variables in the Southern Ocean, which influence its in situ variability. The need for this stems from the need to obtain reliable estimates of carbon dioxide concentrations in the Southern Ocean which plays an important role in the global carbon flux cycle. The main challenge involved in this objective is the spatial limitation and seasonal bias of the in situ data. Moreover, studies have also reported that the relationship between \(pCO_2\) and its drivers is complex. As such, in this paper, we use the non-parametric kernel regression approach since it is able to accurately capture the complex relationships between the response and predictor variables. In this analysis we use the in situ data obtained from the SANAE49 return leg journey between Antarctic to Cape Town. To the best of our knowledge, this is the first time this data set has been subjected to such analysis. The model variants were developed on a training data subset, and the ‘goodness’ of the models were assessed on an “unseen” test data subset. Results indicate that the nonparametric approach consistently captures the relationship more accurately in terms of mean square error, root mean square error and mean absolute error, over a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict \(pCO_2\) for a larger spatial region in the Southern Ocean based on satellite biogeochemical measurements of predictor variables, given that satellites do not measure \(pCO_2\) .     

Thermoplasticized Pectin by Extrusion/Thermo-Compression for Film Industrial Application

Journal of Polymers and the Environment - Researches for the development of materials with less environmental impact have been more common. Pectin is a natural polysaccharide, presenting in plants...     

Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM

Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O₃) and particulate matter was performed using monitoring data from 22 background stations over Portugal. After removing the bias from each model, different ensemble techniques were applied and compared. Besides the median, several weighted ensemble approaches were tested and intercompared: static (SLR) and dynamic (DLR) multiple linear regressions (using less-square optimization method) and the Bayesian Model Averaging (BMA) methodology. The goal of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results. The obtained results revealed that no one of the 4 tested ensembles clearly outperforms the others on the basis of statistical parameters and probabilistic analysis (reliability and resolution properties). Nevertheless, statistical results have shown that the application of the weights slightly improves ensemble performance when compared to those obtained from the median ensemble. The same statistical analysis together with the probabilistic measures demonstrates that the SLR and BMA methods are the best performers amongst the assessed methodologies.