Brown rust disease control in winter wheat: I. Exploring an approach for disease progression based on night weather conditions

An empirical approach for simulating the infection and progress of leaf rust (caused by Puccinia triticina) during stem elongation on winter wheat was analysed for the 2000 to 2006 growing seasons. The approach was elaborated based on night weather conditions (i.e., air temperature, relative humidity and rainfall) and leaf rust occurrences. Data from three consecutive cropping seasons (2000–2002) at four representative sites of the Grand-Duchy of Luxembourg were used in the set-up phase. The capability to correctly simulate the occurrence expression of P. triticina infections on the upper leaf layers was then assessed over the 2003–2006 period. Our study revealed that the development of leaf rust required a period of at least 12 consecutive hours with air temperatures ranging between 8 and 16 °C, a relative humidity greater than 60 % (optimal values being 12–16 °C and up to 80 % for air temperatures and relative humidity, respectively) and rainfall less than 1 mm. Moreover, leaf rust occurrences and infections were satisfactorily simulated. The false alarm ratio was ranged from 0.06 to 0.20 in all the study sites. The probability of detection and critical success index for WLR infection were also close to 1 (perfect score).     

Investigation of novel material for effective photodegradation of bezafibrate in aqueous samples

A novel composite with an enhanced photocatalytic activity was prepared and applied to study the removal of bezafibrate (BZF), a hypolypemic pharmaceutical, from an aqueous environment. For the enhancement of titanium dioxide photoactivity a fullerene derivative, 2-(ferrocenyl) fulleropyrrolidine (FcC₆₀), was synthesized and applied. Obtained composite was found to show a higher catalytic activity than pristine TiO₂. Therefore, high hopes are set in composites that are based on carbonaceous nanomaterials and TiO₂ as a new efficient photocatalysts.     

Endosulfan sulfate sorption on natural organic substances

This work proposes a viable remediation method based on the use of natural organic substances (NOSs) that characterize the Mediterranean region to improve the ecological system. A series of experiments, including variable conditions, such as temperature, pH, contact time, and pesticide concentration, were performed to demonstrate the efficiency of endosulfan sulfate removal from water by NOSs. Experimental results showed that the pH and temperature of pesticide solutions negatively affect the adsorption process. The maximum adsorption capacity for a specific initial concentration of endosulfan sulfate (0.5 microg/L) was achieved with Origanum compactum (75%), followed by Cistus ladaniferus and Raphanus raphanistrum (72 and 68%, respectively). The adsorption tests gave very satisfying results and point to the possible application of these supports as a remediation technique to prevent pesticide contamination of aquatic ecosystems.     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.     

Organochlorine contamination in some marketable fish in Egypt

Ten organochlorine compounds were analyzed in four species of fish (Bouri Mugil spp., Denis Sparus auratus, Moza Boops boops and Mousa Pegusa lascaris), from four Egyptian governorates (South Sinai, Suez, Port-Said and Demietta). Although all fish studied are bottom or near-bottom feeders, remarkable variations between studied species were noticed. The grand total values recorded at the studied governorates were in descending order: Bouri>Denis>Moza>Mousa. The highest concentration of pesticides was recorded in Demietta governorate fish (20-211 ng/g of wet weight) and the p,p'-DDE dominated over the other p,p'-isomers in all studied fish, while dieldrin was the dominated in the studied cyclodiene compounds. However, hexachlorobenzene (HCB) occurred at all studied governorate fish except Mousa fish from South Sinai. Toxaphene was detected only in fish (Bouri, Denis, and Mousa) from Demietta governorate. Chlordane was not detected in any studied fish. The level of organochlorine pesticides contamination in fish from the studied governorates is relatively low and should not pose a health risk to consumers.     

Electrochemical analysis of endosulfan using a C18-modified carbon-paste electrode

Successful applications of different analytical procedures to determine quantitatively endosulfan and its metabolites in aqueous media can be found in recent literature. Fundamentally, they have made use of solid-phase extraction (SPE) and gas (GC) or liquid chromatography (LC), sometimes coupled to mass spectrometry (MS). In this paper, a new and alternative methodology to determine quantitatively endosulfan in aqueous media is reported. A C18-modified carbon-paste electrode has been used to determine voltammetrically endosulfan, despite its unfavourable electrochemical properties and behaviour. The methodology proposed is based on the decrease experienced by the peak intensity corresponding to voltammetric signals of Cu(II) when successive and constant additions of endosulfan are carried out. This decrease is directly proportional to the concentration of endosulfan what allows to perform an indirect quantification of the pesticide. The detection limit obtained is 40 ng l⁻¹, this value being under the limits specified by European norms and EPA reports.     

Natural radioactivity measurements in building materials in Southern Lebanon

Using gamma-spectroscopy and CR-39 detector, concentration C of naturally occurring radioactive nuclides (226)Ra, (222)Rn, (214)Bi, (228)Ac, (212)Pb, (212)Bi and (40)K, has been measured in sand, cement, gravel, gypsum, and paint, which are used as building materials in Lebanon. Sand samples were collected from 10 different sandbank locations in the southern part of the country. Gravel samples of different types and forms were collected from several quarries. White and gray cement fabricated by Shaka Co. were obtained. gamma-spectroscopy measurements in sand gave Ra concentration ranging from 4.2+/-0.4 to 60.8+/-2.2 Bq kg(-1) and Ra concentration equivalents from 8.8+/-1.0 to 74.3+/-9.2 Bq kg(-1). The highest Ra concentration was in gray and white cement having the values 73.2+/-3.0 and 76.3+/-3.0 Bq kg(-1), respectively. Gravel results showed Ra concentration between 20.2+/-1.0 and 31.7+/-1.4 Bq kg(-1) with an average of 27.5+/-1.3 Bq kg(-1). Radon concentration in paint was determined by CR-39 detector. In sand, the average (222)Rn concentration ranged between 291+/-69 and 1774+/-339 Bq m(-3) among the sandbanks with a total average value of 704+/-139 Bq m(-3). For gravel, the range was found to be from 52+/-9 to 3077+/-370 Bq m(-3) with an average value of 608+/-85 Bq m(-3). Aerial and mass exhalation rates of (222)Rn were also calculated and found to be between 44+/-7 and 2226+/-267 mBq m(-2)h(-1), and between 0.40+/-0.07 and 20.0+/-0.3 mBq kg(-1)h(-1), respectively.