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...     

The Influence of Linear Elements on Plant Species Diversity of Mediterranean Rural Landscapes: Assessment of Different Indices and Statistical Approaches

This paper mainly aims to study the linear element influence on the estimation of vascular plant species diversity in five Mediterranean landscapes modeled as land cover patch mosaics. These landscapes have several core habitats and a different set of linear elements -habitat edges or ecotones, roads or railways, rivers, streams and hedgerows on farm land- whose plant composition were examined. Secondly, it aims to check plant diversity estimation in Mediterranean landscapes using parametric and non-parametric procedures, with two indices: Species richness and Shannon index.Land cover types and landscape linear elements were identified from aerial photographs. Their spatial information was processed using GIS techniques. Field plots were selected using a stratified sampling design according to relieve and tree density of each habitat type. A 50×20 m² multi-scale sampling plot was designed for the core habitats and across the main landscape linear elements. Richness and diversity of plant species were estimated by comparing the observed field data to ICE (Incidence-based Coverage Estimator) and ACE (Abundance-based Coverage Estimator) non-parametric estimators.The species density, percentage of unique species, and alpha diversity per plot were significantly higher (p < 0.05) in linear elements than in core habitats. ICE estimate of number of species was 32% higher than of ACE estimate, which did not differ significantly from the observed values. Accumulated species richness in core habitats together with linear elements, were significantly higher than those recorded only in the core habitats in all the landscapes. Conversely, Shannon diversity index did not show significant differences.     

Bioaerosol sampling by a personal rotating cup sampler CIP 10-M

High concentrations of bioaerosols containing bacterial, fungal and biotoxinic matter are encountered in many workplaces, e.g. solid waste treatment plants, waste water treatment plants and sewage networks. A personal bioaerosol sampler, the CIP 10-M (M-microbiologic), has been developed to measure worker exposure to airborne biological agents. This sampler is battery operated; it is light and easy to wear and offers full work shift autonomy. It can sample much higher concentrations than biological impactors and limits the mechanical stress on the microorganisms. Biological particles are collected in 2 ml of liquid medium inside a rotating cup fitted with radial vanes to maintain an air flow rate of 10 l min(-1) at a rotational speed of approximately 7,000 rpm. The rotating cup is made of sterilisable material. The sampled particles follow a helicoidal trajectory as they are pushed to the surface of the liquid by centrifugal force, which creates a thin vertical liquid layer. Sterile water or another collecting liquid can be used. Three particle size selectors allow health-related aerosol fractions to be sampled according to international conventions. The sampled microbiological particles can be easily recovered for counting, incubation or further biochemical analysis, e.g., for airborne endotoxins. Its physical sampling efficiency was laboratory tested and field trials were carried out in industrial waste management conditions. The results indicate satisfactory collection efficiency, whilst experimental application has demonstrated the usefulness of the CIP 10-M personal sampler for individual bioaerosol exposure monitoring.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

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.     

Carbon stocks and losses to deforestation in protected areas in Brazilian Amazonia

Regional Environmental Change - By 2014 approximately 2.2 million km2 (~43%) of Brazil’s Legal Amazonia region had been incorporated into an extensive network of 718 protected areas, which...     

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.     

An integrated approach for assessing the migration behavior of chlorpyrifos and carbaryl in the unsaturated soil zone

Chlorpyrifos (O, O-diethyl O-3,5,6-trichloropyridin-2-yl phosphorothioate) and carbaryl (1-naphthyl methylcarbamate) are often applied concurrently as insecticides in food production. The aim of this study was to research their migration behavior in a real environment. We researched the leaching of both pesticides by setting up field lysimeters on a farm with the typical soil used in fruit production today. In order to analyze the variables involved in this process, we performed complementary adsorption studies, we performed complementary adsorption studies using batches and undisturbed soil laboratory columns for both compounds. The results for pesticide transport through the lysimeters showed that less than 1% of chlorpyrifos was recovered in the leachates, while almost 17% was recovered for carbaryl. Having completed the experiment in undisturbed laboratory columns, soil analysis showed that chlorpyrifos mainly remained in the first 5 cm, while carbaryl moved down to the lower sections. These results can be explained in view of the sorption coefficient values (K_D) obtained in horizons A and B for chlorpyrifos (393 and 184 L kg^?1) and carbaryl (3.1 and 4.2 L kg^?1), respectively. By integrating the results obtained in the different approaches, we were able to characterize the percolation modes of these pesticides in the soil matrix, thus contributing to the sustainable use of resources.     

Development and implementation of eco-genomic tools for aquatic ecosystem biomonitoring: the SYNAQUA French-Swiss program

The effectiveness of environmental protection measures is based on the early identification and diagnosis of anthropogenic pressures. Similarly, restoration actions require precise monitoring of changes in the ecological quality of ecosystems, in order to highlight their effectiveness. Monitoring the ecological quality relies on bioindicators, which are organisms revealing the pressures exerted on the environment through the composition of their communities. Their implementation, based on the morphological identification of species, is expensive because it requires time and experts in taxonomy. Recent genomic tools should provide access to reliable and high-throughput environmental monitoring by directly inferring the composition of bioindicators’ communities from their DNA (metabarcoding). The French-Swiss program SYNAQUA (INTERREG France-Switzerland 2017–2019) proposes to use and validate the tools of environmental genomic for biomonitoring and aims ultimately at their implementation in the regulatory bio-surveillance. SYNAQUA will test the metabarcoding approach focusing on two bioindicators, diatoms, and aquatic oligochaetes, which are used in freshwater biomonitoring in France and Switzerland. To go towards the renewal of current biomonitoring practices, SYNAQUA will (1) bring together different actors: scientists, environmental managers, consulting firms, and biotechnological companies, (2) apply this approach on a large scale to demonstrate its relevance, (3) propose robust and reliable tools, and (4) raise public awareness and train the various actors likely to use these new tools. Biomonitoring approaches based on such environmental genomic tools should address the European need for reliable, higher-throughput monitoring to improve the protection of aquatic environments under multiple pressures, guide their restoration, and follow their evolution.     

Honeybees (<Emphasis Type="Italic">Apis mellifera</Emphasis>) as a biological barrier for contamination of honey by environmental toxic metals

The aim of the study was to investigate the transfer of toxic metals from honeybee workers (Apis mellifera L.) to bee honey in relation to the ecological state of the environment. The materials of the study consisted of samples of honeybee bodies and varietal honeys taken from the same apiary located in three areas: R1—urbanized (16), R2—ecologically clean (16) and R3—industrialized (15) of south-eastern Poland. The contents of 14 elements in all tested samples, including toxic metals (Cd, Pb, Hg, Al, Ni, Tl) as well as bioelements (K, Mg, Ca, Mn, Fe, Zn, Cu, Se), were analysed by the ICP-OES method with prior microwave mineralization. The concentrations of the majority of the studied elements, excluding aluminum and lead, were significantly higher in bee bodies than in honey samples (P?<?0.05). The pollution of bee bodies by toxic metals was dependent on the environmental cleanliness, and the most pollution was observed in the industrialized area. The bee body was the most effective barrier for Cd and Tl transfer to the honey, while the level of Ni was similar in both tested materials. The Al concentration was significantly higher in honey than bee bodies (14.81?±?24.69 and 6.51?±?5.83 mg kg^?1, respectively), which suggests the possibility of secondary contamination of honey. The greatest sensitivity to heavy metal pollution was observed in honeydew honey compared to nectar honeys (P?<?0.05). It was proved for the first time that bees work as biofilters for toxic metals and prevent honey contamination.