ECOPERSIA
Year:2017 | Volume:5 | Issue:4|Papers Count:7

Background: Extreme temperature events can impose serious impacts on environment and societies. Since the outbreak of cold and frost are one of the important factors of climate in many parts of Iran, utilization of a new model for predicting the continuity of these factors is necessary. Materials and Methods: This paper uses high-order categorical non-stationary Markov chains to study the occurrence of extreme cold temperature events by transition and probabilities matrixes in Zabol, southeast of Iran. The occurrence of frost days, homogeneity, continuity and spatial duration were analyzed for 30 years (April 1982-April 2012). The multivariate regression was used to modeling and mapping the statistical characteristics of frost and Kriging interpolation method in Arc/GIS was applied for its relationship. Results: The occurrence of frost days in Zabol was in conformity with Markov model characteristic that showed the continuation of frost days depended on the weather of preceding days. Discussion and Conclusions: Heavy frost in Zabol is expected to occur in Jan and Feb. Thus, frost-free day cycle duration was more than frost cycle and occurrences of frost in short term were more than long term in the studied period.

Background: The issue of airport noise pollution is of paramount importance to communities in the vicinity of airports. Materials and Methods: The potential effects of aircraft noise at the Imam Khomeini International Airport (Iran) was investigated by employing remote sensing and the geographic information system (GIS) in conjunction with an optimization algorithm integrated with CadnaA software. CadnaA is a computer model used to develop noise exposure maps (NEMs) to determine how noise affects a specific area. The results of aircraft noise modeling with this software for three scenarios (in 2015, 2025 and 2035) are provided in the NEMs. A georeferenced GIS database was built in Envi software comprising topography and land use data, the results of the CadnaA model and project data. These maps were overlaid. Face-to-face interviews were carried out by canvassing door-to-door in the permitted survey sites near IKIA and by structural modeling of the questionnaire estimates using AMOS. 7 software. Results: The results showed that the CadnaA model well simulated and predicted noise changes in different scenarios. The results of the map overlay indicate the compatibility of existing land use around the IKIA airport with noise levels and provided alerts against the development of residential areas in the near future. Conclusions: The results of the questionnaires indicate a high LDEN correlation coefficient and irritation levels from aircraft noise. Urban development around the airport as well as an increase in the number of flights and runways at IKIA should be carefully studied.

Background: Along with rapid economic growth, many natural regions, meadows, farms, etc. have been converted into unbridled urban areas. Urban development converts natural areas into districts full of buildings leading to disrupted ecological balance of the ecosystem. The carrying capacity (CC) of urban ecosystems needs to be estimated because they require large amounts of materials and energy as well as the ability of pollutant absorption in a small location. The amount of material and energy used in cities may be more than of that provided by urban CC. High consumption rate is associated with high levels of contamination that transcends the UCC. Therefore, the CC of the urban environment and its population capacity must be evaluated for urban development planning. Materials and Methods: In this study, UCC load number within the pressure-state-impact-response (PSIR) framework and 20 indicators were used to evaluate the CC and pressure on the urban ecosystem of Semnan. Results: According to the results, the load number in the district 1 was equal to 180. 05with a low to moderate pressure on the urban ecosystem. The load numbers in districts 2 and 3 were respectively 230. 41 and 272. 86 imposing a moderate to high pressure on urban ecosystem. Conclusions: Because of the greater population density in the District 3, materials and energy consumption and waste production was higher leading to a higher pressure on the urban ecosystem.

Background: Measures of nutrient availability such as concentrations of carbon (C), nitrogen (N) and phosphorus (P) are important indicators of terrestrial ecosystems productivity. Current research illustrates the C, N and P stoichiometry of litter and soil in a coastal mixed forest stand, northern Iran. Materials and Methods: To this, the Carpinus betulus (CB), Acer velutinum (AV), Pterocarya fraxinifolia (PF), Quercus castaneifolia (QC) species were considered; litter and soil (0-15cm depth) samples were taken under tree canopy cover. Results: Litter and soil C: N ratio differed among the tree species, showing the highest (61. 08 and 31. 44) and lowest (21. 90 and 3. 59) under the QC and CB tree species, respectively. The litter and soil C: P ratio varied among the study sites and ranked in order of QC (52. 4 and 27227. 04) > PF (30 and 1465. 61) > AV (15. 74 and 630. 54) ≈ CB (13. 42 and 566. 28). The higher amounts of litter N: P ratio were significantly found under QC (0. 86) > PF (0. 73) > CB (0. 61) ≈ AV (0. 55), whereas soil N: P ratio were significantly higher under CB (177. 69) > PF (123. 53) ≈ AV (121. 60) > QC (109. 25), respectively. Conclusion: We found the species that differed in traits could influence C, N and P dynamics and its stoichiometry. The Q. castaneifolia species with different root traits that resulted in different vertical and horizontal distributions of C, N and P, reflecting differences in nutrient uptake by plants and microbial dynamics, drove the biggest changes in litter and soil C, N and P.

Background: Seed energy and seed vigour are the most important qualitative attributes influencing plant’ s growth and establishment that can be improved by techniques generally known as seed priming, which enhances the percentage, speed and uniformity of germination. Effect of various priming techniques was conducted on seed germination and seedling’ s early growth of elder pine (Pinus eldarica Medw. ) in Seed Technology Lab of Natural Resources Faculty, Tarbiat Modares University, Iran. Materials and Methods: Seeds were treated through hydropriming with distilled water, halopriming with NaCl at-4 and-8 bar concentrations, osmopriming with polyethylene glycol 6000 (PEG 6000) at-4 and-8 bar concentrations and hormonopriming with salicylic acid (SA) at 1 and 2 mM solutions for 48 h. Un-primed dry seeds were taken as control. The seeds were kept in germinator at 20 ± 0. 5 ° C, 65% relative humidity and 16. 8 h light/dark photoperiod for 42 days. Results: The highest germination percentage (92%) and germination speed (5. 13 seeds/day) were obtained with hydropriming. The best results to improve germination energy, time to 50% germination, seedling length, seedling dry weight and seedling vigour index were achieved with hydropriming and hormonalpriming 1 and 2 mM. Osmopriming and halopriming-8 bar compared to control in most mentioned traits showed poor performance. Conclusions: Hydropriming and hormonalpriming can be suitable techniques to support nursery practices of elder pine seed in order to improve germination percentage, emergence and early seedling growth.

Background: Concentrations of 4 toxic metals, viz. Cd, Cu, Pb, and Zn in the soil and alfalfa samples collected from Borujerd, Iran, was determined. The capability of alfalfa to accumulate heavy metals from soils was assessed in terms of Biological Concentration Factor. Materials and Methods: The alfalfa and soil samples were collected from 20 different farms, including 13 wastewater-irrigated and seven underground-irrigated farms. After acid digestion, the samples were analyzed using atomic absorption spectrophotometer. Results: The levels of Cd, Pb, and Zn in the soils of wastewater-irrigated farms were higher than those from the groundwater-irrigated farms. With the exception of Cu, concentrations of heavy metals in the alfalfa crop were higher in wastewater-irrigated farms compared to well water. Also, in the case of BCF, both Cd and Cu values decreased with increasing metal concentration in soil. The order of BCF of heavy metals in alfalfa was in order of Cu>Cd>Zn>Pb in well water-irrigated and Zn>Cd>Cu>Pb in wastewater – irrigated samples. Discussion and Conclusions: The findings remarked that the levels of Cu, Cd, and Pb in alfalfa were exceeding the permissible levels suggested by the Joint FAO/WHO Expert Committee on Food Additives. These outcomes propose that the consumption of alfalfa plants is potentially threatening both animal and human health.

Background: Prediction of future climate change is based on output of global climate models (GCMs). However, because of coarse spatial resolution of GCMs (tens to hundreds of kilometers), there is a need to convert GCM outputs into local meteorological and hydrological variables using a downscaling approach. Downscaling technique is a method of converting the coarse spatial resolution of GCM outputs at the regional or local scale. This study proposed a novel hybrid downscaling method based on artificial neural network (ANN) and particle swarm optimization (PSO) algorithm. Materials and Methods: Downscaling technique is implemented to assess the effect of climate change on a basin. The current study aims to explore a hybrid model to downscale monthly precipitation in the Minab basin, Iran. The model was proposed to downscale large scale climatic variables, based on a feed-forward ANN optimized by PSO. This optimization algorithm was employed to decide the initial weights of the neural network. The National Center for Environmental Prediction and National Centre for Atmospheric Research reanalysis datasets were utilized to select the potential predictors. The performance of the artificial neural network-particle swarm optimization model was compared with artificial neural network model which is trained by Levenberg– Marquardt (LM) algorithm. The reliability of the models were evaluated by using root mean square error and coefficient of determination (R2). Results: The results showed the robustness and reliability of the ANN-PSO model for predicting the precipitation which it performed better than the ANN-LM. It was concluded that ANN-PSO is a better technique for statistically downscaling GCM outputs to monthly precipitation than ANN-LM. Discussion and Conclusions: This method can be employed effectively to downscale large-scale climatic variables to monthly precipitation at station scale.