BackgroundElimination of hazardous phenolic compounds using laccases has gained attention during recent decades. The present study was designed to evaluate the ability of the purified laccase from Paraconiothyrium variabile (PvL) for elimination of phenol and the endocrine disrupting chemical bisphenol A. Effect of laccase activity, pH, and temperature on the enzymatic removal of the mentioned pollutants were also investigated.ResultsAfter 30 min treatment of the applied phenolic pollutants in the presence of PvL (5 U/mL), 80% of phenol and 59.7% of bisphenol A was removed. Increasing of laccase activity enhanced the removal percentage of both pollutants. The acidic pH of 5 was found to be the best pH for elimination of both phenol and bisphenol A. Increasing of reaction temperature up to 50°C enhanced the removal percentage of phenol and bisphenol A to 96.3% and 88.3%, respectively.ConclusionsTo sum up, the present work introduced the purified laccase of P. variabile as an efficient biocatalyst for removal of one of the most hazardous endocrine disruptor bisphenol A.

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The present work was conducted in a two-part study. In part I, the levels of indoor and outdoor PM 10 , PM 2.5 , and PM 1 was measured using real time GRIMM dust monitors. In part II, the performance of NAIs method was investigated on reduction of indoor concentration of PM in these residential buildings for the first time. Hourly average concentration and standard deviation (SD) of PM 10 in indoor and outdoor at residential buildings were 63.5 ± 27.4 and 90.1 ± 33.5 µg/m 3 , respectively. Indoor and outdoor concentrations of PM 2.5 in residential buildings were 39.4 ± 18.1 and 49.5 ± 18.2 µg/m 3 and for PM 1 the concentrations were 4.3 ± 7.7 and 6.5 ± 10.1 µg/m 3 , respectively. We estimated that nearly 71.47% of PM 10 , 79.86% of PM 2.5 and of 61.25% of PM 1 in indoor of residential buildings can be removed by negative air ions.

Sediment samples from the coastal area of Asaluyeh harbor were collected during autumn and spring 2015. The acidvolatile sulfide (AVS) and simultaneously extracted metals (SEMs) were measured to assess the sediment quality and potential ecological risks. The average concentrations (and relative standard deviation (RSD)) of AVS in the industrial sediments were 12.32 μmol/g (36.91) and 6.34 μmol/g (80.05) in autumn and spring, respectively, while in the urban area, these values were 0.44 μmol/g (123.50) and 0.31 μmol/g (160.0) in autumn and spring, respectively. The average concentrations of SEM (and RSD) in the industrial sediments were 15.02 μmol/g (14.38) and 12.34 μmol/g (20.65) in autumn and spring, respectively, while in the urban area, these values were 1.10 μmol/g (43.03) and 1.06 μmol/g (55.59) in autumn and spring, respectively. Zn was the predominant component (34.25-86.24 %) of SEM, while the corresponding value for Cd, much more toxic ingredient, was less than 1 %. Some of the coastal sediments in the harbor of Asaluyeh (20 and 47 % in autumn and spring, respectively) had expected adverse biological effects based on the suggested criterion by United States Environmental Protection Agency (USEPA), while most stations (80 and 53 % in autumn and spring, respectively) had uncertain adverse effects.

Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020–2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.

BACKGROUND: Fabrication of nanocomposites with magnetic core and their applications

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