Investigation of the association between pesticide exposure and the prevalence of type 2 diabetes in Shahedieh population in Yazd

Environmental Science and Pollution Research - This study aimed to investigate the effect of exposure to pesticides and the prevalence of type 2 diabetes (T2D). In a cross-sectional study with a...     

COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION1

ABSTRACT: Two dynamic programming models — one deterministic and one stochastic — that may be used to generate reservoir operating rules are compared. The deterministic model (DPR) consists of an algorithm that cycles through three components: a dynamic program, a regression analysis, and a simulation. In this model, the correlation between the general operating rules, defined by the regression analysis and evaluated in the simulation, and the optimal deterministic operation defined by the dynamic program is increased through an iterative process. The stochastic dynamic program (SDP) describes streamflows with a discrete lag-one Markov process. To test the usefulness of both models in generating reservoir operating rules, real-time reservoir operation simulation models are constructed for three hydrologically different sites. The rules generated by DPR and SDP are then applied in the operation simulation model and their performance is evaluated. For the test cases, the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules are more effective for the operation of small reservoirs.     

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.     

Modeling carbon sequestration under zero tillage at the regional scale. I. The effect of soil erosion

Zero tillage is recognized as a potential measure to sequester carbon dioxide in soils and to reduce CO₂ emissions from arable lands. An up-scaling approach of the output of the Environmental Policy Integrated Climate (EPIC) model with the information system SLISYS-BW has been used to estimate the CO₂-mitigation potential in the state of Baden-Württemberg (SW-Germany). The state territory of 35,742 km² is subdivided into eight agro-ecological zones (AEZ), which have been further subdivided into a total of 3976 spatial response units. Annual CO₂-mitigation rates where estimated from the changes in soil organic carbon content comparing 30 years simulations under conventional and zero tillage. Special attention was given to the influence of tillage practices on the losses of organic carbon through soil erosion, and consequently on the calculation of CO₂-mitigation rates. Under conventional tillage, mean carbon losses through erosion in the AEZ were estimated to be up to 0.45 Mg C ha⁻¹ a⁻¹. The apparent CO₂-mitigation rate for the conversion from conventional to zero tillage ranges from 0.08 to 1.82 Mg C ha⁻¹ a⁻¹ in the eight AEZ, if the carbon losses through soil erosion are included in the calculations. However, the higher carbon losses under conventional tillage compared to zero tillage are composed of both, losses through enhanced CO₂ emissions, and losses through intensified soil erosion. The adjusted net CO₂-mitigation rates of zero tillage, subtracting the reduced carbon losses through soil erosion, are between 0.07 and 1.27 Mg C ha⁻¹ a⁻¹ and the estimated net mitigation rate for the entire state amounts to 285 Gg C a⁻¹. This equals to 1045 Gg CO₂-equivalents per year with the cropping patterns in the reference year 2000. The results call attention to the necessity to revise those estimation methods for CO₂-mitigation which are exclusively or predominantly based on the measurements of differential changes in total soil organic carbon without taking into account the tillage effects on carbon losses through soil erosion.     

Influence of polymethyl acrylate additive on the formation of particulate matter and NOX emission of a biodiesel–diesel-fueled engine

Environmental Science and Pollution Research - The aim of this study is to investigate the effect of the polymethyl acrylate (PMA) additive on the formation of particulate matter (PM) and nitrogen...     

Cattle egrets as a biosentinels of persistent organic pollutants exposure

We investigated selected chlorinated pollutants (β-HCH, γ-HCH, DDDs, DDEs, o,p′-DDT, p,p′-DDT, heptachlor, aldrin, dieldrin, and endrin) in the Lahore and the Sialkot districts of Pakistan, using eggs of cattle egret (Bubulcus ibis) collected during May and June 2007. The pollutant with highest level and frequency was ΣDDT, followed by β-HCH, γ-HCH, heptachlor, aldrin, dieldrin, and endrin in descending order. The concentration(s) were significantly higher in Sialkot heronry for all the pollutants (except p,p′-DDT) than in Lahore. The values for DDTs, β-HCH, γ-HCH, and heptachlor were significantly higher (p < 0.05) in the egg(s) than in sediment(s) and in the chicks’ diet, due to biomagnification. Among DDTs analogues, p,p′-DDD was the major contaminant with >60 % of total DDT burden, reflecting the widespread aged as well as recent use of DDT as well as anaerobic degradation (DDD/DDE > 1 in many cases) in the nearby paddy soils. In few samples, p,p′-DDT/(DDD + DDE) > 0.5 suggested the recent emission patterns from surrounding contaminated areas of demolished DDT units and obsolete pesticide stores. The higher levels of HCHs (i.e., β-HCH) in the samples collected from Sialkot indicate exposure from long-term agricultural use. Overall, concentrations of all studied POPs were less than the threshold levels known to affect reproduction. Nevertheless, total DDTs and/or HCHs burdens in some eggs contained concentrations of greater than what would educe adverse effects on birds. This is among few studies on OCPs exposure to avian species, which provide the evidence of Pakistan’s contribution toward the Global POPs emission.     

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.     

Role of plant growth promoting rhizobacteria in the remediation of metal contaminated soils

Pollution of the biosphere by the toxic metals is a global threat that has accelerated dramatically since the beginning of industrial revolution. The primary source of this pollution includes the industrial operations such as mining, smelting, metal forging, combustion of fossil fuels and sewage sludge application in agronomic practices. The metals released from these sources accumulate in soil and in turn, adversely affect the microbial population density and physico-chemical properties of soils, leading to the loss of soil fertility and yield of crops. The heavy metals in general cannot be biologically degraded to more or less toxic products and hence, persist in the environment. Conventional methods used for metal detoxification produce large quantities of toxic products and are cost-effective. The advent of bioremediation technology has provided an alternative to conventional methods for remediating the metal-poisoned soils. In metal-contaminated soils, the natural role of metal-tolerant plant growth promoting rhizobacteria in maintaining soil fertility is more important than in conventional agriculture, where greater use of agrochemicals minimize their significance. Besides their role in metal detoxification/removal, rhizobacteria also promote the growth of plants by other mechanisms such as production of growth promoting substances and siderophores. Phytoremediation is another emerging low-cost in situ technology employed to remove pollutants from the contaminated soils. The efficiency of phytoremediation can be enhanced by the judicious and careful application of appropriate heavy-metal tolerant, plant growth promoting rhizobacteria including symbiotic nitrogen-fixing organisms. This review presents the results of studies on the recent developments in the utilization of plant growth promoting rhizobacteria for direct application in soils contaminated with heavy metals under a wide range of agro-ecological conditions with a view to restore contaminated soils and consequently, promote crop productivity in metal-polluted soils across the globe and their significance in phytoremediation.