Spatial analysis of resources and environmental carrying capacity in Iran

Urbanization and mass movement of the population from rural areas and small cities to megacities have led to environmental, economic, and social problems in Iran. In dealing with these challenges, assessing resource and environmental carrying capacity (RECC) is considered an effective method to leverage space and capital to achieve sustainable development. This study aimed to rank the provincial RECC in Iran. Toward this purpose, environmental indices were generated from remotely sensed and statistical census data. Then, the provinces were scored in terms of environmental, economic, and infrastructural carrying capacities, and RECC using the mean variance analysis method. Results demonstrated that in most areas, there is no relationship between economic and infrastructural capacities and development. Statistically, a correlation coefficient of −0.53 between economic and environmental carrying capacities indicated excessive use of environmental capacities. Moreover, the spatial distribution pattern of environmental, economic, and infrastructural carrying capacity was entirely heterogeneous between the provinces; there was a northeast–southwest pattern in terms of infrastructural capacity and an economic pattern from north to south. The distribution pattern of RECC is most consistent with the environmental capacity, pointing at the high weight of the indicators of the RECC model. In conclusion, this research offers a new vision for policymakers and provides a theoretical and applicable framework for implementing sustainable strategies in land-use planning. It is recommended that the RECC concept and tools can be used not only for planning but also for measuring the efficiency of spatial development programs and establishing land balances in the region.     

Multi-objective conflict resolution optimization model for reservoir’s selective depth water withdrawal considering water quality

Environmental Science and Pollution Research - This paper develops a multi-objective conflict resolution simulation-optimization model based on a leader-follower game to resolve conflicts between...     

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.     

Bioaccumulation of trace elements in different tissues of three commonly available fish species regarding their gender,gonadosomatic index,and condition factor in a wetland ecosystem

In the present research, accumulation of Cd, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn were determined in sediments and fillet, liver, kidney, gonads, and gills of three commonly fish species in the largest wetland ecosystem that is located in southwest of Iran; Shadegan wetland. Shadegan is one of the most important wetland that posses various fauna and flora but suffers inputs from agricultural and industrial activities. So, sediment samples and fish species including Barbus grypus, Barbus sharpeyi, and Cyprinus carpio were collected during winter 2009. Results showed that mean concentrations of trace elements (except Ni and Co) were high in liver and gills of B. grypus. Also trace elements had the most accumulation in liver of B. sharpeyi except for Cd (0.26 mg kg^− 1 d.w.) and Mn (13.45 mg kg^− 1 d.w.) that were high in gills. Beside, kidney is determined as target tissue for Ni and V in B. grypus and for Pb in C. carpio, due to their high concentration. Zn levels in all tissues of C. carpio showed the highest concentrations in comparison to other fish species. Generally, accumulations of most of the studied elements in B. grypus and B. sharpeyi were higher in females than in males, while in fillet and gonads of C. carpio, this trend was inverted. Bioaccumulation factors (BAFs) were determined for different tissues of fish species with respect to elemental concentrations in sediment. BAFs results indicated that Zn, Pb, and Cu have higher BAF than other elements. Also this investigation demonstrated that trace elements have different affinities with condition factor of studied fish species. Gonadosomatic index (GSI) and Pb showed positive correlation together in both B. sharpeyi and B. grypus, respectively, in females and males. Moreover, females of C. carpio showed significantly positive relation of GSI and all studied elements.     

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.     

Bio-indicators in cadmium toxicity: Role of HSP27 and HSP70

Environmental Science and Pollution Research - Heat shock proteins (HSPs) are a family of proteins that are expressed by cells in reply to stressors. The changes in concentration of HSPs could be...     

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.