Marine organisms as heavy metal bioindicators in the Persian Gulf and the Gulf of Oman

In the present study, cadmium and lead concentrations were compared in barnacles, ghost shrimps, polychaetes, bivalves, and sediment from ten different locations along the intertidal zone of the Persian Gulf and the Gulf of Oman. The results revealed significant differences in the heavy metal concentrations between the organisms with barnacles showing, by far, the highest metal concentrations. The bioaccumulation factor of Cd in different animals follows this pattern with barnacles?>?bivalves?>?polychaetes?>?ghost shrimps, while the pattern for Pb was barnacles?>?polychaetes?>?bivalves?>?ghost shrimps. In most of the stations, sediments showed the lowest lead and cadmium concentrations. Therefore, it is concluded that barnacles with Pb concentrations between 0.17 and 2,016.1 μg/g and Cd concentrations ranging from 0.4 to 147.1 μg/g are the best organisms to be employed in monitoring programs designed to assess pollution with bioavailable metals in the Persian Gulf and the Gulf of Oman.     

Heavy metals partitioning in sediments of the Kabini River in South India

Cu, Cr, Fe, Mn, Ni, Pb, and Zn in the sediments of the Kabini River, Karnataka, India was studied to determine the association of metal with various geochemical phases by sequential extraction. The variations of heavy metal concentration depend on the lithology of the river basin and partly on anthropogenic activities. The Kabini River sediments are dominated by Sargur supracrustals with amphibolites, gneisses, carbonates, and ultrabasic rocks weathering into gneissic and serpentine soils carrying a natural load of cationic heavy metals. The source of heavy metals in the Kabini riverbed sediments is normally envisaged as additional inputs from anthropogenic over and above natural and lithogenic sources. Geochemical study indicates the metals under study were present mostly in the least mobilizable fraction in the overlying water and it is concluded that heavy metals in these sediments are to a great extent derived from multisource anthropogenic inputs besides geochemical background contributions The results show that lead and chromium have higher potential for mobilization from the sediment due to higher concentration at the exchangeable ion and sulfide ion bounded, also Cu and Pb have the greatest percentage of carbonate fraction, it means that the study area received inputs from urban and industrial effluents. Association of the Fe with organic matter fraction can be explained by the high affinity of these elements for the humic substances. Further, Zn and Ni reveal a significant enrichment in sediment and it is due to release of industrial wastewater into the river. These trace metals are possible contaminants to enter into aquatic and food chain.     

Epidemiology of occupational accidents among Iranian insured workers

Work-related accidents are considered as a major health problem worldwide. The aim of present study was to determine the epidemiological pattern of occupational accidents among workers insured by the Iranian Social Security Organization (ISSO) between 2001 and 2005.Subjects consisted of all workers and drivers who had a work-related accident during 2001–2005 and were registered in the Iranian Social Security Organization (ISSO) database according to the inspection reports. An ordinal logistic regression model (proportional odds regression model) was used to assess the concurrent effects of independent variables on accident outcomes.Overall, 86,437 work-related accidents were investigated. The accidents were more frequent in metal workplaces and electrical industries, respectively. More than half of the accidents were due to incautious activities. Workers’ age (age at the time of accident) (OR = 0.99, CI: 0.989–0.994), gender (OR = 1.3, CI: 1.191–1.683), marital status (OR = 1.25, CI: 1.143–1.675) as well as accident setting (OR = 1.88, CI: 1.728–1.975) had significant effect on accident outcomes. Pattern of occupational injuries in Iran was consistent with the global pattern for accident outcomes.     

Bootstrap confidence intervals for adaptive cluster sampling design based on Horvitz–Thompson type estimators

Perez and Pontius (J Stat Comput Simul 76:755–764, 2006) introduced several bootstrap methods under adaptive cluster sampling using a Horvitz–Thompson type estimator. Using a simulation study, they showed that their proposed methods provide confidence intervals with highly understated coverage rates. In this article, we first show that their bootstrap methods provide biased bootstrap estimates. We then define two bootstrap methods, based on the method of Gross (Proceeding of the survey research methods section. American Statistical Association, Alexandria, VA, pp 181–184, 1980) and Bootstrap With Replacement, that provide unbiased bootstrap estimates of the population mean with bootstrap variances matching the corresponding unbiased variance estimator. Using a simulation study, we show that the bootstrap confidence intervals based on our proposed methods have better performance than those based on available bootstrap methods, in the sense of having coverage proportion closer to the nominal coverage level. We also compare the proposed intervals to empirical likelihood based intervals in small samples.     

Regression-type estimators for adaptive two-stage sequential sampling

Adaptive two-stage sequential sampling (ATSSS) design was developed to observe more rare units and gain higher efficiency, in the sense of having a smaller variance estimator, than conventional sampling designs with equal effort for rare and spatially cluster populations. For certain rare populations, incorporating auxiliary variables into a sampling design can further improve the observation of rare units and increase efficiency. In this article, we develop regression-type estimators for ATSSS so that auxiliary variables can be incorporated into the ATSSS design when warranted. Simulation studies on two populations show that the regression-type estimators can significantly increase the efficiency of ATSSS and the detection of more rare units as compared to conventional sampling counterparts. Simulation of sampling of desert shrubs in Inner Mongolia (one of the two populations studied) showed that by incorporating a GIS auxiliary variable into ATSSS with the regression estimators resulted in a gain in efficiency over ATSSS without the auxiliary variable. Further, we found that the use of the GIS auxiliary variable in a conventional two-stage design with a regression estimator did not show a gain in efficiency.     

Empirical likelihood confidence intervals for adaptive cluster sampling

Adaptive cluster sampling (ACS) is an efficient sampling design for estimating parameters of rare and clustered populations. It is widely used in ecological research. The modified Hansen-Hurwitz (HH) and Horvitz-Thompson (HT) estimators based on small samples under ACS have often highly skewed distributions. In such situations, confidence intervals based on traditional normal approximation can lead to unsatisfactory results, with poor coverage properties. Christman and Pontius (Biometrics 56:503–510, 2000) showed that bootstrap percentile methods are appropriate for constructing confidence intervals from the HH estimator. But Perez and Pontius (J Stat Comput Simul 76:755–764, 2006) showed that bootstrap confidence intervals from the HT estimator are even worse than the normal approximation confidence intervals. In this article, we consider two pseudo empirical likelihood functions under the ACS design. One leads to the HH estimator and the other leads to a HT type estimator known as the Hájek estimator. Based on these two empirical likelihood functions, we derive confidence intervals for the population mean. Using a simulation study, we show that the confidence intervals obtained from the first EL function perform as good as the bootstrap confidence intervals from the HH estimator but the confidence intervals obtained from the second EL function perform much better than the bootstrap confidence intervals from the HT estimator, in terms of coverage rate.     

A new approach for layout optimization in maintenance workshops with safety factors: The case of a gas transmission unit

In this study, an Integrated Simulation-Data Envelopment Analysis (DEA) approach is presented for optimum facility layout of maintenance workshop in a gas transmission unit. The process of repair of incoming parts includes various operations on different facilities. The layout problem in this system involves determining the optimum location of all maintenance shop facilities. Layout optimization plays a crucial role in this type of problems in terms of increasing the efficiency of main production line. Standard types of layouts including U, S, W, Z and straight lines are considered. First, the maintenance workshop is modeled with discrete-event-simulation. Time in system, average waiting time, average machine utilization, average availability of facilities, average queue length of facilities (AL) and average operator utilization are obtained from simulation as key performance indicators (KPIs) of DEA. Also, safety index and number of operators are considered as other KPIs. Finally, a unified non-radial Data Envelopment Analysis (DEA) is presented with respect to the stated KPIs to rank all layouts alternatives and to identify the best configuration. Principle Component Analysis (PCA) is used to validate and verify the results. Previous studies do not consider safety factor in layout design problems. This is the first study that presents an integrated approach for identification of optimum layout in a maintenance workshop of gas transmission unit by incorporating safety and conventional factors.     

Use of post-stratification in composite sampling for estimating mean

We show with the results of a study conducted in the Hamadan Province, Iran as to how the use of composite sampling for estimating mean zinc concentration in the soil can help reduce analytical costs by reducing the number of analysis required. We also introduce post-stratification methodology in the composition step to take advantage of possible spatial dispersion. We speculate that the zinc concentration value depends on the sample location, we first stratified the sample set and then composite units from different strata randomly. The results of a simulation study show that the use of this approach not only reduces the total costs but also increases the precision of the estimator.     

Heavy metal pollution in water and sediments in the Kabini River, Karnataka, India

Critical to habitat management is the understanding of not only the location of animal food resources, but also the timing of their availability. Grizzly bear (Ursus arctos) diets, for example, shift seasonally as different vegetation species enter key phenological phases. In this paper, we describe the use of a network of seven ground-based digital camera systems to monitor understorey and overstorey vegetation within species-specific regions of interest. Established across an elevation gradient in western Alberta, Canada, the cameras collected true-colour (RGB) images daily from 13 April 2009 to 27 October 2009. Fourth-order polynomials were fit to an RGB-derived index, which was then compared to field-based observations of phenological phases. Using linear regression to statistically relate the camera and field data, results indicated that 61% (r (2)?= 0.61, df = 1, F?= 14.3, p?= 0.0043) of the variance observed in the field phenological phase data is captured by the cameras for the start of the growing season and 72% (r (2)?= 0.72, df = 1, F?= 23.09, p?= 0.0009) of the variance in length of growing season. Based on the linear regression models, the mean absolute differences in residuals between predicted and observed start of growing season and length of growing season were 4 and 6?days, respectively. This work extends upon previous research by demonstrating that specific understorey and overstorey species can be targeted for phenological monitoring in a forested environment, using readily available digital camera technology and RGB-based vegetation indices.     

Adsorptive Removal of Lead from Water Using a Novel Cysteine-Bentonite/Poly(vinyl alcohol)/Alginate Nanocomposite

Journal of Polymers and the Environment - Adsorptive removal of toxic metals from water using materials with high removal capability and sufficient environmental degradability is of great...