Mangroves are coastal ecosystems, found in tropical and subtropical regions around the world. They are found in the transitional zones between land, sea, and rivers. Petroleum hydrocarbons are the most common environmental pollutants, and oil spills pose a great hazard to mangroves forests. This research was focused on the isolation and characterization of crude oil‐degrading bacteria from mangrove ecosystems at the Persian Gulf. Sixty‐one crude oil‐degrading bacteria were isolated from mangrove samples (plant, sediment, and seawater) that enriched in ONR7a medium with crude oil as only carbon source. Some screening tests such as growth at high concentration of crude oil, bioemulsifier production, and surface hydrophobicity were done to select the most efficient strains for crude oil degradation. Molecular identification of strains was carried out by amplification of the 16S rRNA gene by PCR. The results of this study were indicated that the quantity of crude oil‐degrading bacteria was higher in the root of mangrove plants compare to other mangrove samples (sediment and seawater). Also, identification results confirmed that these isolated strains belong to Vibrio sp. strain NW4, Idiomarina sp. strain BW32, Kangiella sp. strain DP40, Marinobacter sp. strain DW44, Halomonas sp. strain BS53, and Vibrio sp. strain DS35. The application of bioremediation strategies with these bacteria can reduce crude oil pollution in this important marine environment. 相似文献
Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model. 相似文献
The effects of salinity on the copepod, Acartia tonsa in terms of daily egg production rate (EPR), hatching success, fecal pellet production rate (FPR), naupliar development time and survival, sex ratio, and total life span were determined in laboratory conditions through three experiments. In experiment 1, EPR, hatching success, and FPR of individual females were monitored at salinities of 13, 20, 35 and 45 during short-periods (seven consecutive days). Results show EPR was affected by salinity with the highest outputs recorded at 20 and 35, respectively, which were considerably higher than those at 13 and 45. Mean FPR was also higher in 35 and 20. In experiment 2, the same parameters were evaluated over total life span of females (long-term study). The best EPR and FPR were observed in 35, which was statistically higher than at 13 and 20. In experiment 3, survival rates of early nauplii until adult stage were lowest at a salinity of 13. The development time increased with increasing of salinity. Female percentage clearly decreased with increasing salinity. Higher female percentages (56.7% and 52.2%, respectively) were significantly observed at two salinities of 13 and 20 compared to that at 35 (25%). Total longevity of females was not affected by salinity increment. Based on our results, for mass culture we recommend that a salinity of 35 be adopted due to higher reproductive performances, better feeding, and faster development of A. tonsa. 相似文献
Due to the explosive industrialization and rapid expansion of the population in many parts of the world, heavy metals are released into the environment continuously and pose a great risk on human health. Street dust and surface soil samples from very heavy, heavy, medium and low traffic areas and a natural site in Tehran, Iran, were analyzed for some physicochemical features, total and chemical fractionating of selected metals (Zn, Al, Sr, Pb, Cu, Cr, Cd, Co, Ni and V) to investigate the influence of traffic on their mobility and accumulation in the environment. The pH, electrical conductivity (EC), carbonates and organic carbon contents were similar in soil and dust samples from the areas with same traffic. The traffic increases EC contents in dust/soil matrixes, but has no effect on concentrations of metals in soil samples. Rises in metal levels with traffic were found in dust samples. Moreover, the traffic increases the percentage of both acid-soluble and reducible fractions, which are related to Pb and Zn. The mobilization of Cu, Zn, Pb, Cr in dust samples was easier than in soil. The speciation of metals except Cd is mainly affected by physicochemical features in soil, though total metals affected the speciation in dust samples (except chromium and nickel). 相似文献
Design of reinforced soil structures is greatly influenced by soil–geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil–anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil–geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the Gene Expression Programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil–anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (σn) and anchorage length (L) and the least effective parameters are average particle size (D50) and anchor base area (Ab).