Is ionizing radiation effective in removing pharmaceuticals from wastewater?

Environmental Science and Pollution Research - Wastewater and effluent discharges are the main causes of receiving water body pollution and important challenges in water quality management. Among...     

Waterborne agrichemicals compromise the anti-predatory behavior of zebrafish

Environmental Science and Pollution Research - Due to human activities, there is an increasing presence of agrochemicals residues in water bodies, which could be attributed to an increased use of...     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.     

Ovariectomy predisposes female rats to fine particulate matter exposure’s effects by altering metabolic,oxidative, pro-inflammatory,and heat-shock protein levels

Environmental Science and Pollution Research - The reduction of estrogen levels, as a result of menopause, is associated with the development of metabolic diseases caused by alterations in...     

Dynamic effects of fiscal and monetary policy instruments on environmental pollution in ASEAN

Environmental Science and Pollution Research - This study aims to re-examine the impacts of monetary and fiscal policy on environmental quality in ASEAN countries from 1990 to 2019. We utilized the...     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

Water quality index for agricultural systems in Northwest Uruguay

Agricultural systems have experienced rapid expansion and intensification in the last several decades. In Uruguay, since the beginning of 2000, the most common cropping systems have included soybeans. Currently, this crop is expanding towards lowlands traditionally occupied by rice in rotation with pastures. However, the environmental effects of agricultural intensification and diversification are not well known. Thus, some indices have been proposed to quantify the changes in agricultural production systems and assess water quality. The main goal of this study was to develop a water quality index (WQI) to assess the impacts of the diversification of rice production systems in northwest Uruguay. The study was carried out in an agricultural basin where other summer crops have been incorporated in the rice-pasture sequence. Agriculture intensification and crop diversification indices were calculated using information provided by farmers. Water samples were collected downstream of the production area before crop sowing and after crop harvest (2008–2009 to 2010–2011 and 2016–2017 to 2017–2018). Biochemical oxygen demand, nitrates, total phosphorus, fecal coliforms, and total suspended solids were the variables that mainly explained the effects of the agricultural activities on water quality. The proposed water quality index included these unweighted variables, which allowed for the pre-sowing and post-harvest to be differentiated, as well as the degree of diversification. Therefore, the proposed WQI constitutes a tool that can be used to evaluate the water quality in an agricultural basin. Likewise, it can be used to select agricultural sequences that generate the least possible impacts on the associated water resources.     

Environmental Impact Assessment of Polylactide(PLA)/Chicken Feathers Biocomposite Materials

The aim of this study is to analyse the environmental impacts (EIs) of the process of preparation of new biocomposite materials obtained from polylactide (PLA) and chicken feathers (CFs). Two CFs stabilization methods and different percentages of CFs have been studied. The EIs of these new composites were compared to the impact of virgin PLA. Cradle-to-gate life cycle inventories were assessed for 0–35% v/v of CFs in a CFs/PLA biocomposite. Two CFs stabilization processes, autoclave and surfactant, were tested and compared with the aim to prioritize one of them from the environmental point of view. A composite plate of 184?×?184?×?2.2 mm³ was defined as the functional unit. Autoclave stabilization process exhibited lower environmental impact compared with surfactant stabilization process mainly due to both the lower requirements of electricity and water and the reduced pollution loads of the generated wastewater. Thus, the autoclave process was selected as the standard method when comparing the EIs of the proposed CFs/PLA biocomposites. In this sense, the addition of CFs to PLA matrix proportionally reduces all the EIs compared to pure PLA due to the replacement of PLA with CFs. This behaviour can be explained because the PLA production accounts for the 99% of the impact of the biocomposite. Consequently, CFs conveniently stabilized might be an alternative raw material to prepare CFs/PLA biocomposites with less environmental impact compared to pure PLA.     

Water Remediation: PVA-Based Magnetic Gels as Efficient Devices to Heavy Metal Removal

Scientific and technological researches are devoted to obtain materials capable of retaining different kinds of pollutants, contributing to contamination solutions. In this context, hydrogels have emerged as great candidates because of their excellent absorption properties as well as good mechanical, thermal and chemical properties. More specifically, ferrogels (magnetic gels) present the extra advantage of being easily manipulated by a permanent magnet. Here, we present the results derived from the application of ferrogels as efficient tools to extract heavy metal pollutants from wastewater samples. The gels were prepared following the method of freezing and thawing of a polyvinyl alcohol aqueous solution with magnetic nanoparticles coated with polyacrylic acid. Ferrogels were fully characterized and their ability to retain Cu²⁺ and Cd²⁺, as model heavy metals, was studied. Thus kinetics and mechanisms of adsorption were evaluated and modeled. The concentration of MNPs on the PVA matrix was key to improve the adsorption capability (approximately the double of retention is improved by the MNPs addition). The adsorption kinetics was determined as pseudo-second order model, whereas the Langmuir model was the most appropriate to explain the behavior of the gels. Finally reuse ability was evaluated to determine the real potential of these materials, the ferrogels demonstrated high efficiency up to about five cycles, retaining about 80–90% of their initial adsorption capability. All the results indicated that the materials are promising candidates able to compete with the commercial technology regarding to water remediation.