Adaptation strategies for maize cultivation under climate change in Iran: irrigation and planting date management

Climate change is affecting the productivity of crops and their regional distribution. Strategies to enhance local adaptation capacity are needed to mitigate climate change impacts and to maintain regional stability of food production. The objectives of this study were to simulate the climate change effects on phenological stages, Leaf Area Index (LAI), biomass and grain yield of maize (Zea mays L.) in the future and to explore the possibilities of employing irrigation water and planting dates as adaptation strategies to decrease the climate change impacts on maize production in Khorasan Razavi province, Iran. For this purpose, we employed two types of General Circulation Models ((United Kingdom Met. Office Hadley Center: HadCM3) and (Institute Pierre Simon Laplace: IPCM4)) and three scenarios (A1B, A2 and B1). Long Ashton Research Station-Weather Generator (LARS-WG) was used to produce daily climatic parameters as one stochastic growing season for each projection period. Also, crop growth under projected climate conditions was simulated based on the Cropping System Model (CSM)-CERES-Maize. The results of model evaluation showed that LARS-WG had appropriate prediction for climatic parameters. Time period from cultivation until anthesis and maturity were reduced in majority of scenarios as affected by climate change. The results indicated that the grain yield of maize may be reduced (11 % to 38 %) as affected by climate change based on common planting date in baseline and changed (?61 % to 48 %) in response to different irrigation regimes in the future climate change, under all scenarios and times. In general, earlier planting date (1 May) and decreasing irrigation intervals in the anthesis stage (11 applications) caused higher yield compared with other planting dates due to adaption to high temperature. Based on our findings, it seems that management of irrigation water and planting dates can be beneficial for adaptation of maize to climate change in this region.     

CoFe2O4@Methylcelloluse as a New Magnetic Nano Biocomposite for Sonocatalytic Degradation of Reactive Blue 19

Journal of Polymers and the Environment - Reactive Blue 19 (RB19) removal from synthetic textile wastewater was investigated by using a CoFe2O4@methylcellulose (MC) activated with peroxymonosulfate...     

Health effects associated with long-term occupational exposure of employees of a chlor-alkali plant to mercury

This study aimed to evaluate possible health effects associated with long-term occupational exposure to low levels of mercury vapors. Forty-six subjects exposed to mercury and 65 healthy unexposed employees were studied. The subjects were administered a questionnaire on experienced symptoms and underwent clinical examinations as well as routine biochemical tests. Atmospheric and urinary concentrations of mercury were measured, too. Environmental concentrations of mercury were estimated to be 3.97 ± 6.28 μg/m(3) and urinary concentrations of mercury in exposed and referent groups were 34.30 ± 26.77 and 10.15 ± 3.82 μg/dm(3), respectively. Additionally, symptoms such as somatic fatigue, anorexia, loss of memory, erethism, blurred vision and teeth problems were significantly more common among exposed individuals. These observations indicate that occupational exposure to mercury vapors, even at low levels, is likely to be associated with neurological and psychological symptoms.     

Evaluation of EHS hazard and sustainability metrics during early process design stages using principal component analysis

A principal component analysis (PCA) based methodology accounting for EHS hazard and sustainability metrics has been recently proposed in literature (Srinivasan and Nhan, 2008) to deal with the subjective weighting problem of existing index-based methods. In this study we evaluate the potential use of the PCA-based method during early phases of process design in the problem of selection between various synthesis paths, also called chemical routes, for the production of chemical compounds. The study also focuses on the impact of the methodology settings on the obtained chemical route rankings and their interpretation. Two case studies have been performed regarding the production of 4-(2-methoxyethyl)-phenol (MEP) and methyl methacrylate (MMA) using fifteen different evaluation categories capturing various sustainability metrics. The PCA-based method identified the most promising chemical routes as well as the most important evaluation categories. The necessity for normalization of the raw data was demonstrated, without the method being very sensitive to the type of normalization. Moreover, the effect of the transition approach from chemical step to chemical route scores is discussed. The results of the PCA-based method are also compared with an index-based method (Koller et al., 2000) sharing the same evaluation categories, as well as with other index-based frameworks in order to reveal the extent of similarities.     

Preconcentration of tin in environmental and biological samples by ion exchange using modified Amberlite XAD-2

Utilization of Amberlite XAD-2 surface modified by covalent immobilization of brilliant green through an azo spacer for adsorptive enrichment of Sn(II) from environmental and biological samples was highlighted. The resulting resin was characterized by Fourier transform infrared spectroscopy, elemental analysis, thermogravimetric analysis, and scanning electron microscopy. The resin retained Sn(II) ions at an optimum pH of 9.5 with a sorption capacity of 40 mg g^?1. The modified sorbent could be reused for 10 cycles without significant changes in sorption capacity. The recovery of Sn(II) was 98% when eluted with 0.1 mol L^?1 ethylenediaminetetraacetic acid. Scatchard analysis revealed that binding sites in the modified resin were homogeneous. The equilibrium adsorption data were analyzed using the Langmuir, Freundlich, Temkin, and Redlich–Peterson isotherm models. The method was applied with satisfactory results for determination of Sn(II) ions in human plasma and sea water.     

A ternary CdS/AgBr/Ag3PO4 nanocomposite: characterization and the kinetics of its photocatalytic activity

Environmental Science and Pollution Research - A ternary CdS/AgBr/Ag3PO4 coupled system was prepared, characterized by different techniques, and used for the photodegradation of methylene blue...     

GIS-based statistical model for the prediction of flood hazard susceptibility

At present, flood is the most significant environmental problem in the entire world. In this work, flood susceptibility (FS) analysis has been done in the Dwarkeswar River basin of Bengal basin, India. Fourteen flood causative factors extracted from different datasets like DEM, satellite images, geology, soil and rainfall data have been considered to predict FS. Three heuristic models and one statistical model fuzzy Logic (FL), frequency ratio (FR), multi-criteria decision analysis (MCDA) and logistic regression (LR) have been used. The validating datasets are used to validate these models. The result shows that 68.71%, 68.7%, 60.56% and 48.51% area of the basin is under the moderate to very high FS by the MCDA, FR, FL and LR, respectively. The ROC curve with AUC analysis has shown that the accuracy level of the LR model (AUC?=?0.916) is very much successful to predict the flood. The rest of the models like FL, MCDA and FR (AUC?=?0.893, 0.857 and 0.835, respectively) have lesser accuracy than the LR model. The elevation was the most dominating factor with coefficient value of 19.078 in preparation of the FS according to the LR model. The outcome of this study can be implemented by local and state authority to minimize the flood hazard.

     

Subnational exposure to secondhand smoke in Iran from 1990 to 2013: a systematic review

Environmental Science and Pollution Research - Every year, almost eight million people die from tobacco-related diseases, among which around 1.2 million die from secondhand smoke (SHS) exposure....     

Adaptation of maize to climate change impacts in Iran

Adaptation is a key factor for reducing the future vulnerability of climate change impacts on crop production. The objectives of this study were to simulate the climate change effects on growth and grain yield of maize (Zea mays L.) and to evaluate the possibilities of employing various cultivar of maize in three classes (long, medium and short maturity) as an adaptation option for mitigating the climate change impacts on maize production in Khorasan Razavi province of Iran. For this purpose, we employed two types of General Circulation Models (GCMs) and three scenarios (A1B, A2 and B1). Daily climatic parameters as one stochastic growing season for each projection period were generated by Long Ashton Research Station-Weather Generator (LARS?WG). Also, crop growth under projected climate conditions was simulated based on the Cropping System Model (CSM)-CERES-Maize. LARS-WG had appropriate prediction for climatic parameters. The predicted results showed that the day to anthesis (DTA) and anthesis period (AP) of various cultivars of maize were shortened in response to climate change impacts in all scenarios and GCMs models; ranging between 0.5 % to 17.5 % for DTA and 5 % to 33 % for AP. The simulated grain yields of different cultivars was gradually decreased across all the scenarios by 6.4 % to 42.15 % during the future 100 years compared to the present climate conditions. The short and medium season cultivars were faced with the lowest and highest reduction of the traits, respectively. It means that for the short maturing cultivars, the impacts of high temperature stress could be much less compared with medium and long maturity cultivars. Based on our findings, it can be concluded that cultivation of early maturing cultivars of maize can be considered as the effective approach to mitigate the adverse effects of climate.