Characterizing relationships between personal exposures to VOCs and socioeconomic,demographic, behavioral variables

Socioeconomic and demographic factors have been found to significantly affect time-activity patterns in population cohorts that can subsequently influence personal exposures to air pollutants. This study investigates relationships between personal exposures to eight VOCs (benzene, toluene, ethylbenzene, o-xylene, m-,p-xylene, chloroform, 1,4-dichlorobenzene, and tetrachloroethene) and socioeconomic, demographic, time-activity pattern factors using data collected from the 1999–2000 National Health and Nutrition Examination Survey (NHANES) VOC study. Socio-demographic factors (such as race/ethnicity and family income) were generally found to significantly influence personal exposures to the three chlorinated compounds. This was mainly due to the associations paired by race/ethnicity and urban residence, race/ethnicity and use of air freshener in car, family income and use of dry-cleaner, which can in turn affect exposures to chloroform, 1,4-dichlorobenzene, and tetrachloroethene, respectively. For BTEX, the traffic-related compounds, housing characteristics (leaving home windows open and having an attached garage) and personal activities related to the uses of fuels or solvent-related products played more significant roles in influencing exposures. Significant differences in BTEX exposures were also commonly found in relation to gender, due to associated significant differences in time spent at work/school and outdoors. The coupling of Classification and Regression Tree (CART) and Bootstrap Aggregating (Bagging) techniques were used as effective tools for characterizing robust sets of significant VOC exposure factors presented above, which conventional statistical approaches could not accomplish. Identification of these significant VOC exposure factors can be used to generate hypotheses for future investigations about possible significant VOC exposure sources and pathways in the general U.S. population.     

Characterization of phosphogypsum wastes associated with phosphoric acid and fertilizers production

The present work is directed to characterize the phosphogypsum (PG) wastes associated with phosphoric acid produced by the wet process in industrial facility for the production of fertilizers and chemicals in Egypt. The PG waste samples were characterized in terms of spectroscopic analysis (X-ray diffraction, X-ray fluorescence, IR spectra) and radiometric analysis (γ- and α-measurements). The γ-ray measurements showed that the average activity concentrations are 140 ± 12.6, 459 ± 36.7, 323 ± 28.4, 8.3 ± 0.76 and 64.3 ± 4.1 Bq/kg for U-238, Ra-226, Pb-210, Th-232 and K-40, respectively. The α-particle measurements of uranium isotopes showed that the average activity concentrations of U-238, U-235 and U-234 were 153 ± 9.8, 7 ± 0.38, 152 ± 10.4 Bq/kg, respectively. The average radiochemical recovery (%) of the destructive α-particle measurements is ∼70% with a resolution (FWHM) of ∼30 keV. Activity ratios of U-238/Ra-226 and U-238/Pb-210 were less than unity (i.e., <1) and equal to 0.31 ± 0.02 and 0.47 ± 0.16, respectively. The isotopic ratios of U-238/U-235 and U-238/U-234 (in PG and PR samples) were close to the normal values of ∼21.7 and ∼1, respectively and are not affected by the wet processing of phosphate rock (PR). The obtained results of PG waste samples were compared with phosphate rock (PR) samples. The radiation hazard indices are namely, radium activity index (Ra-Eq > 370 Bq/kg), total absorbed gamma dose rate (D_γr > 5 nGy/h) and radon emanation fraction (Rn-EF > 20%). Uncertainty of the sample counting was 95% confidence level of σ. The results indicated the necessity to find suitable routes to decrease and/or redistribute the radionuclide of environmental interest (i.e., Ra-226) in PG wastes, consequently to reduce its radiation impacts in the surrounding environment.     

Reviewer Acknowledgments

Acknowledgment

Reviewer Acknowledgments     

The effect of mycorrhiza on the growth and elemental composition of Ni-hyperaccumulating plant Berkheya coddii Roessler

The effect of arbuscular mycorrhizal fungi (AMF) on growth and element uptake by Ni-hyperaccumulating plant, Berkheya coddii, was studied. Plants were grown under laboratory conditions on ultramafic soil without or with the AM fungi of different origin. The AM colonization, especially with the indigenous strain, significantly enhanced plants growth and their survival. AMF affected also the elemental concentrations that were studied with Particle-induced X-ray emission (PIXE). AMF (i) increased K and Fe in shoots, Zn and Mn in roots, P and Ca both, in roots and shoots; (ii) decreased Mn in shoots, Co and Ni both, in shoots and roots. Due to higher biomass of mycorrhizal plants, total Ni content was up to 20 times higher in mycorrhizal plants compared to the non-mycorrhizal ones. The AMF enhancement of Ni uptake may therefore provide an improvement of a presently used technique of nickel phytomining.     

Toxic effects of arsenic on Sinorhizobium-Medicago sativa symbiotic interaction

Recently, the Rhizobium-legume symbiotic interaction has been proposed as an interesting tool in bioremediation. However, little is known about the effect of most common contaminants on this process. The phytotoxic effects of arsenic on nodulation of Medicago sativa have been examined in vitro using the highly arsenic resistant and symbiotically effective Sinorhizobium sp. strain MA11. The bacteria were able to grow on plates containing As concentrations as high as 10 mM. Nevertheless, as little as 25-35 microM arsenite produced a 75% decrease in the total number of nodules, due to a 90% reduction in the number of rhizobial infections, as could be determined using the strain MA11 carrying a lacZ reporter gene. This effect was associated to root hair damage and a shorter infective root zone. However, once nodulation was established nodule development seemed to continue normally, although earlier senescence could be observed in nodules of arsenic-grown plants.     

The potential effect of temperature-humidity index on productive and reproductive performance of buffaloes with different genotypes under hot conditions

The current study was aimed to investigate the impact of THI on productive and reproductive indices of PE and F₁ crosses (50% PE and 50% Italian buffaloes) and back crosses (BC) (75% PE and 25% Italian buffaloes) under hot conditions. In this study, 8385 records used PE (1914, 1518, and 1737), F₁ (387, 447, and 657), and BC (495, 585, and 645) for low, medium, and high THI, respectively. The high THI reduced the conception rate after first insemination in PE, F₁, and BC ((odds ratio, OR) = 1.187, P = 0.007; 2.361, <0.0001 and 1.603, <0.0001, respectively) when compared with low THI. But, stillbirth and calving condition were not significantly influenced by different THI levels in BC and F₁. BC was more bearable to the harsh environment; they possessed the highest incidence of conception after first insemination (72.70, 72.60, and 62.40%), producing live calves (98.50, 100, and 99.40%) with easy calving condition (98.50, 100, and 99.40%) when compared to PE and F₁ at different levels of THI, respectively. The average daily milk yield and peak of milk production were decreased in PE (4.02 and 5.12%), BC (13.33 and 10.95%), and F₁ (25.29 and 12.20%) from low to high THI. However, BC revealed no significant changes in days open, dry period, calving interval, gestation length, service per conception, and the first service post-partum at different levels of THI when compared with PE and F₁. Therefore, rearing BC is recommended for improving buffalo productive and reproductive performance under hot conditions.     

New Methods for the Preparation Nanosized Metal Coordination Polymers

Transition-metal coordination polymers [M(ndc)(bpy)·(H₂O)m]·xH₂O (where M = Co(II), Ni(II), Cu(II) and Zn(II), ndc = 1,4-naphthalenedicarboxylate, bpy = 4,4′-bipyridine; m = 0 or 1; x = 1 or 2) were prepared by reacting the ligands and metal ions at room temperature with the aid of microwave irradiation and sonication methods. The structure of the coordination polymers was assigned based on elemental analysis, FT-IR and electronic spectral studies, thermal analysis, scanning electron microscope and X-ray powder diffraction. Thermogravimetric analysis was also used to follow up the possible thermal decomposition steps and to calculate the thermodynamic parameters of the nano-sized metal complexes. The kinetic parameters were calculated making use of the Coats–Redfern and Horowitz–Metzger equations. All obtained results of the different technics used in our study stated the ability to synthesis the metal coordination polymers of our interest using both microwave irradiation and sonication methods.     

Adaptation planning for sea level rise: a study of US coastal cities

Sea level rise (SLR) is expected, even without greenhouse gas emissions. As SLR is inevitable, adapting to its impacts has received increasing attention, and local governments are the key actors in this emerging agenda. This study indicates that adaptation planning for SLR should be integrated into two local major planning mechanisms in the United States: the local comprehensive plans and hazard mitigation plans. By evaluating 36 plans from 15 US coastal cities that are considered at high risk and vulnerable to rising sea levels, the results demonstrate that SLR is widely identified, but the overall quality of the plans to address it requires significant improvement. A detailed table of selected plans' characteristics is also provided as a lens on how localities tackle this challenging issue. The paper concludes with planning suggestions for coastal communities to better adapt to SLR.     

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Knowing the fraction of methane (CH₄) oxidized in landfill cover soils is an important step in estimating the total CH₄ emissions from any landfill. Predicting CH₄ oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH₄ oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O₂) concentration at a depth of 10 cm in cover soil, and CH₄ concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH₄ oxidation with a MSE of 0.0082, a coefficient of determination (R ²) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.

Implications:

To date, no attempts have been made to predict the percent of CH₄ oxidation within landfill cover soils using an ANN. This paper presents modeling of CH₄ oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH₄ oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH₄ oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH₄ emission and how much it contributes to global warming.