A cost-effective screening method for pesticide residue analysis in fruits, vegetables, and cereal grains

This paper reports the results of studies performed to investigate the potential of applying thin layer chromatography (TLC) detection in combination with selected extraction and cleanup methods, for providing an alternative cost-effective analytical procedure for screening and confirmation of pesticide residues in plant commodities. The extraction was carried out with ethyl acetate and an on-line extraction method applying an acetone-dichloromethane mixture. The extracts were cleaned up with SX-3 gel, an adsorbent mixture of active carbon, magnesia, and diatomaceous earth, and on silica micro cartridges. The Rf values of 118 pesticides were tested in eleven elution systems with UV, and eight biotest methods and chemical detection reagents. Cabbage, green peas, orange, and tomatoes were selected as representative sample matrices for fruits and vegetables, while maize, rice, and wheat represented cereal grains. As an internal quality control measure, marker compounds were applied on each plate to verify the proper elution and detection conditions. The Rf values varied in the different elution systems. The best separation (widest Rf range) was achieved with silica gel (SG)--ethyl acetate (0.05-0.7), SG--benzene, (0.02-0.7) and reverse phase RP-18 F-254S layer with acetone: methanol: water/30:30:30 (v/v) (0.1-0.8). The relative standard deviation of Rf values (CV(Rf)) within laboratory reproducibility was generally less than 20%, except below 0.2 Rf, where the CVRf rapidly increased with decreasing Rf values. The fungi spore inhibition, chloroplast inhibition, and enzyme inhibition were found most suitable for detection of pesticides primarily for confirming their identity or screening for known substances. Their use for determination of pesticide residues in samples of unknown origin is not recommended.     

What can the Allium cepa test say about pesticide safety? A review

Environmental Science and Pollution Research - The increasing use of pesticides has caused global concerns about the toxic effects and adverse consequences of pesticides on humans and the...     

Environmental impact of a coal combustion-desulphurisation plant: abatement capacity of desulphurisation process and environmental characterisation of combustion by-products

The fate of trace elements in a combustion power plant equipped with a wet limestone flue gas desulphurisation (FGD) installation was studied in order to evaluate its emission abatement capacity. With this aim representative samples of feed coal, boiler slag, fly ash, limestone, FGD gypsum and FGD process water and wastewater were analysed for major and trace elements using the following techniques: inductively coupled plasma-mass spectrometry (ICP-MS), inductively coupled plasma-atomic emission spectrometry (ICP-AES), ion chromatography (IC), ion selective electrode (ISE) and atomic absorption spectroscopy (AAS). Mass balances were established allowing to determine the element partitioning behaviour. It was found that, together with S, Hg, Cl, F, Se and As were those elements entering in the FGD plant primarily as gaseous species. The abatement capacity of the FGD plant for such elements offered values ranged from 96% to 100% for As, Cl, F, S and Se, and about 60% for Hg. The environmental characterisation of combustion by-products (boiler slag, fly ash and FGD gypsum) were also established according to the Council Decision 2003/33/EC on waste disposal. To this end, water leaching tests (EN-12457-4) were performed, analysing the elements with environmental concern by means of the aforementioned techniques. According to the leaching behaviour of combustion by-products studied, these could be disposed of in landfills for non-hazardous wastes.     

Maritime ports and beach management as sources of coastal macro-, meso-, and microplastic pollution

Environmental Science and Pollution Research - Plastic pollution is a major environmental issue that affects coasts all around the world. Many studies point out the importance of a better...     

Methodology for Identifying Optimal Locations of Water Quality Sensors in River Systems

A method to optimally site river water quality sensors is expanded and applied to a case study river to explore the application of mathematical siting methods to the design of river sampling networks. Fecal coliform contamination due to flooded swine waste lagoons was modeled as it moves downstream, and optimal sensor locations are located by minimizing the objective function of the optimization problem. The results of the simulations are analyzed by varying the number of allowed sampling locations and the simulated contamination event. For the case study application, the model suggests three sampling locations along the modeled river section. These three suggested sensing points did not greatly vary in location for different river flows and contamination events, indicating the robustness of the model results for this specific case study. Generally, the application of mathematical contaminant modeling is a useful and systematic approach to aid the design of river water quality monitoring networks.     

Short- and long-term importance of small sharks in the diet of the rare deep-sea shark Dalatias licha

Knowing the trophic ecology of marine predators is essential to develop an understanding of their ecological role in ecosystems. Research conducted on deep-sea and threatened shark species is limited. Here, by combining analyses of individual stomach contents and stable isotope values, we examined the trophic ecology (dietary composition and trophic position) of the kitefin shark Dalatias licha, a deep-sea shark considered as near threatened globally and as data deficient in the Mediterranean Sea. Results revealed the importance of small sharks in the diet of the kitefin shark at short- and long-term scales, although fin-fish, crustaceans and cephalopods were also found. Predation on sharks reveals the high trophic position of the kitefin shark within the food web of the western Mediterranean Sea. Stable isotope values from liver and muscle tissues confirmed our results from stomach content analysis and the high trophic position.     

CLFUG: A GIS‐scaleable model of pesticide fate in the soil–groundwater system based on clearance and fugacity paradigms

A model of pesticide transport through the soil profile based on clearance and fugacity paradigms is presented, and an example of its application in a GIS environment is shown. A validation of the model at the field plot scale is presented using data obtained at a crop in a semiarid irrigated agricultural basin which was treated with Lindane. The adequacy at the regional scale is tested by inspection of the model predictions and the measured concentrations of the pesticide obtained from a regional phreatimetric net. The clearance concept is used to obtain estimates of the volumes of some environmental phases. These are further used to solve the equations of thermodynamic equilibrium at equal fugacity and obtain concentration estimates. The model closely reproduces the observed percolation trends, and is consistent with the regional pattern of Lindane distribution in groundwater. An application of the model as unitary module for the simulation of non‐point pesticide sources in a raster GIS frame is shown. Its performance (run time, data needed, etc.) is comparable to that of other existing algorithms, and presents some advantages to planners and evaluators of environmental quality in that it incorporates an explicit 2‐D approach and allows the identification of polluted areas downslope with respect to those directly treated with the pesticides. Further, it can be implemented in a variety of GIS and spatial data processors.     

An unforgettable event: a qualitative study of the 1997–98 El Niño in northern Peru

During the 1997–98 El Niño, Tumbes, Peru received 16 times the annual average rainfall. This study explores how Tumbes residents perceived the impact of the El Niño event on basic necessities, transport, health care, jobs and migration. Forty‐five individuals from five rural communities, some of which were isolated from the rest of Tumbes during the event, participated in five focus groups; six of these individuals constructed nutrition diaries. When asked about events in the past 20 years, participants identified the 1997–98 El Niño as a major negative event. The El Niño disaster situation induced a decrease in access to transport and health care and the rise in infectious diseases was swiftly contained. Residents needed more time to rebuild housing; recover agriculture, livestock and income stability; and return to eating sufficient animal protein. Although large‐scale assistance minimized effects of the disaster, residents needed more support. Residents' perspectives on their risk of flooding should be considered in generating effective assistance policies and programmes.     

Induction of glutathione reductase enzyme activity by β‐aminobutyric acid in plant leaves

Abstract

Treatment of pea and tobacco leaf discs with the resistance inducer DL‐β‐amino‐n‐butyric acid (BABA) led to a substantial induction of glutathione reductase (GR, E.C. 1.6.4.2.) enzyme activity. After exposure to 1 mM BABA for 96 hrs, the GR activities were 3.2‐fold and 2.9‐fold higher in pea and tobacco leaf discs, respectively, than GR activities in untreated controls. Elevated GR levels may contribute to the antioxidative protection of plants during pathogen attack.     

An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative—log [NO₂], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity—and qualitative—working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa’s Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R ²?=?0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75–0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.