Mg-Fe layered double hydroxide with chloride intercalated: synthesis,characterization and application for efficient nitrate removal

Environmental Science and Pollution Research - Mg-Fe layered double hydroxide intercalated with chloride (Mg-Fe-Cl LDH) was synthetized, characterized, and evaluated as adsorbent to remove nitrate...     

Cadmium behavior in a karst environment hydrological cycle

Environmental Science and Pollution Research - Karst regions are important water providers, supplying approximately 25% of the world population. These areas present higher vulnerability to...     

“Modern agriculture” transfers many pesticides to watercourses: a case study of a representative rural catchment of southern Brazil

Environmental Science and Pollution Research - The total cultivated area in Brazil reached to 62 million ha in 2018, with the predominance of genetically modified soybean and corn (36 and 17...     

Discrete-choice experiments valuing local environmental impacts of renewables: two approaches to a case study in Portugal

Despite the often mentioned environmental benefits associated with transition from fossil fuels to renewable energy sources, their use for electricity production has non-negligible negative environmental impacts. The most commonly mentioned in surveys concern different types of landscape impacts, impacts on the fauna and flora, and noise. These impacts differ by size and location of plants, and by source of energy, rendering the policy decision complex. In addition, there are other welfare issues to take into consideration, as positive and negative environmental impacts are not evenly distributed among population groups. This paper proposes to compare the welfare impacts of renewable energy sources controlling for the type of renewable as well as the specific environmental impact by source. To this end, two discrete-choice experiments are designed and applied to a national sample of the Portuguese population. In one case, only individual negative impacts of renewables are used, and in another case, the negative impacts interact with a specific source. Results show the robustness of discrete-choice experiments as a method to estimate the welfare change induced by the impacts of renewable energy sources. Overall, respondents are willing to pay to reduce the environmental impacts, thus making compensation for local impacts feasible. Moreover, the estimations reveal that respondents are significantly sensitive to the detrimental environmental effects of specific renewable energy sources, being willing to pay more to use these sources of energy relative to others.     

Contamination of soil and the medicinal plant <Emphasis Type="Italic">Phyllanthus niruri</Emphasis> Linn. with cadmium in ceramic industrial areas

Phyllanthus niruri is a plant that is used to prevent calcium oxalate crystallisation and to block the stone formation in urolithiasis. Contaminants in the environment can be readily taken up by medicinal plants due to their ability to absorb chemicals into their tissues. If contaminated plants are ingested, they have the potential to negatively affect human and environmental health. The aim of this study was to assess contamination in the soil and the medicinal plant P. niruri by cadmium (Cd) in ceramic industrial areas of Monte Carmelo, Brazil. Soil samples and plant samples (divided in root, shoot and leaves) were collected from a contaminated monitoring site and from a rural area (which was used as a reference site for comparative purposes). The Cd concentrations of the samples were analysed with an atomic absorption spectrometer. P. niruri was found to be sensitive to soil contamination by Cd that was attributed to ceramic industrial emissions. The results revealed that Cd bioaccumulation in the roots and shoots of P. niruri was associated with a significant increase (p?<?0.05) in the concentration of active lignan compounds (phyllanthin and hypophyllanthin) in the leaves. The identification of high concentrations of Cd and active lignan compounds suggests a risk of contamination of the site and the risk of a high dose of Cd to people exposed at the site.     

Spectral trend of vegetation with rainfall in events of El Niño-Southern Oscillation for Atlantic Forest biome,Brazil

This study aimed to analyze the spectral trend of vegetation with rainfall in El Niño-Southern Oscillation events (ENSO) in the Atlantic Forest, Brazil. Monthly rainfall data were collected from 85 conventional meteorological stations (EMC), data from the Enhanced Vegetation Index 2 (EVI2) and ENSO events (El Niño, La Niña, and Neutral) in the period from 2001 to 2013. Afterwards, state cluster analysis was performed using the results of non-parametric tests. The Mann-Kendall (MK) non-parametric test did not identify a trend pattern in rainfall distribution in the Atlantic Forest. The results for EVI2 by state and region showed that the trend is decreasing in the Northeast Region, except for the states of Alagoas and Pernambuco. Southeast region showed an increasing trend of EVI2 (except for Rio de Janeiro and São Paulo), while the South region showed a decreasing trend. In the Midwest, the trend was significantly decreasing. In the prognosis elaborated for the future, the regions with significant declines of the vegetation were the Northeast and Midwest. This study shows that the Atlantic Forest in some regions of Brazil has been suffering from the growing urbanization process and there is a trend of soil degradation.     

Influence of different textile fibers on characterization of dyeing wastewater and final effluent

Textile industry needs to recover and reuse its wastewater as to fulfil the demand of increasingly strict regulations. The characterization of dyeing wastewater samples according to textile fiber and final textile effluent enables the application of different treatment methods. This study aims to characterize dyeing wastewater in black color of polyamide, polyester, and viscose fibers and final textile effluent. Samples were collected and characterized completely for major pollution indicator parameters. Dyeing wastewater of polyester showed higher values for some parameters, e.g., 4994.44% (49,944,400 mg L^?1 and 917 NTU) of turbidity and 4100.00% of phenol when compared to dyeing wastewater of other fibers. Other parameters such as pH, alkalinity, color, phosphorus, nitrogen, sulfides, chlorides, oil and grease, dissolved solids, and chemical and biochemical oxygen demand were also assessed. In addition to individual characterization, this study also presents a correlation of the contribution of each parameter to the final textile effluent. Although dyeing wastewater of polyamide contributes the most in terms of quantity for the final effluent, this study revealed that dyeing wastewater of polyester influenced the most on the final composition of the textile wastewater when evaluating color, turbidity, total iron, biochemical oxygen demand, chemical oxygen demand, phenol, mercury, oil and grease, and total phosphorus. The present study is focused on bringing new insights to provide future research with other strategies to improve the treatment of dyeing wastewater. In addition, some suggestions are also given for wastewater treatments according to type of textile fiber.     

FIA-coupled spectrophotometric method for determination of Cr (VI) traces in natural waters: application of in-line dissolution of 1,5-diphenylcarbazide after heat treatment and activated alumina as adsorbent for preconcentration

This work proposes the quantification of Cr (VI) ions in natural waters in trace level, using activated alumina (Al₂O₃) as preconcentration support, controlled in-line dissolution of the solidified chromophore diphenylcarbazide after heat treatment and spectrophotometric detection. The manifold ensures high sensitivity of analytical response, good repeatability, and stability. In this work, optimization of experimental conditions of a flow injection system was chosen as the parameters for greater sensitivity and better selectivity. The selected optimized conditions were 0.30 mol L^?1 for H₂SO₄ concentration, system flow rate as 0.40 mL min^?1, sample injection volume of 192.50 μL, 2 min for preconcentration time, and 0.10 mol L^?1 for eluent concentration. The analytical curves obtained for real sample analysis show linear range from 0.192 to 0.961 μM, linear correlation coefficient R?=?0.9997 and LOD?=?0.024 μM. The preconcentration factor of about four times was obtained through the passage of 800 μL of a standard solution containing 0.961 μM of Cr (VI) through mini-column of preconcentration followed by elution at 192.5 μL of NH₄OH 0.1 mol L^?1 solution. The solid chromogenic reagent presented high durability (weeks in daily use with mass of 0.0993 g) and good reproducibility in analytical signal. The reactivation of the mini-column of alumina should be executed after ten injections of eluent, using 800 μL of HCl 0.02 mol L^?1 solution in flow through the same. Each cycle of injection and elution of the sample takes about 5 min on the proposed terms. Despite the length of each cycle still be high, low concentrations can be detected using a technique of relatively low cost. This is due in part, the association dissolution of the chromogenic reagent directly in the line and the preconcentration step. Another important factor is the economy of reagent chromogenic, low generation of reject contributing to better quality of the environment, and the high potential for applications to work in field.     

Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides

This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative Structure–Property Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanol–water partition coefficient, and higher lipophilicities would lead to compounds having higher Henry’s law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the Levenberg–Marquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions.