Effect of nitrogen fertilizer application on growing season soil carbon dioxide emission in a corn-soybean rotation

Nitrogen application can have a significant effect on soil carbon (C) pools, plant biomass production, and microbial biomass C processing. The focus of this study was to investigate the short-term effect of N fertilization on soil CO(2) emission and microbial biomass C. The study was conducted from 2001 to 2003 at four field sites in Iowa representing major soil associations and with a corn (Zea mays L.)-soybean (Glycine max L. Merr.) rotation. The experimental design was a randomized complete block with four replications of four N rates (0, 90, 180, and 225 kg ha(-1)). In the corn year, season-long cumulative soil CO(2) emission was greatest with the zero N application. There was no effect of N applied in the prior year on CO(2) emission in the soybean year, except at one of three sites, where greater applied N decreased CO(2) emission. Soil microbial biomass C (MBC) and net mineralization in soil collected during the corn year was not significantly increased with increase in N rate in two out of three sites. At all sites, soil CO(2) emission from aerobically incubated soil showed a more consistent declining trend with increase in N rate than found in the field. Nitrogen fertilization of corn reduced the soil CO(2) emission rate and seasonal cumulative loss in two out of three sites, and increased MBC at only one site with the highest N rate. Nitrogen application resulted in a reduction of both emission rate and season-long cumulative emission of CO(2)-C from soil.     

Spillover effects of trade and foreign direct investment on economic growth: an implication for sustainable development

Environment, Development and Sustainability - This paper explores the advantages of international trade and foreign direct investment (FDI), which can be the main factors of transferring technology...     

Exposure to methyl methacrylate and its subjective symptoms among dental technicians, Tehran, Iran.

Exposure to methyl methacrylate (MMA), total dust and health symptoms were investigated in 20 dental laboratories located in Tehran, Iran. Time-weighted average (TWA) of MMA and peak concentrations were determined, using XAD-2 tubes followed by GC-FID analysis. Total dusts were evaluated gravimetrically. Health symptoms were asked using a questionnaire. TWA for technicians with direct and indirect exposure to MMA were 327.28 +/- 79.42 and 282.9 +/- 41.84 mg/m3, respectively. Peak concentration of MMA for those technicians were 337.0 +/- 36.81 and 328.88 +/- 45.40 mg/m3, respectively. There were no significant differences between TWA of MMA and peak concentration in different weekly workdays; however, within-day variations were observed (P < .05). TWA of MMA and peak concentration correlation with the laboratory volume were 0.61-0.65. Dust exposure of technicians was 2.35 +/- 2.70 mg/m3. Cough and skin dryness were the common health symptoms. Smoking and asbestos exposure history were factors influencing cough prevalence (p < .05). It is concluded that the current Short-Term Exposure Limit (STEL) is not low enough to protect technicians against the adverse effects caused by MMA.     

Toward a robust analytical method for separating trace levels of nano-materials in natural waters: cloud point extraction of nano-copper(II) oxide

Cloud point extraction (CPE) factors, namely Triton X-114 (TX-114) concentration, pH, ionic strength, incubation time, and temperature, were optimized for the separation of nano-sized copper(II) oxide (nCuO) in aqueous matrices. The kinetics of phase transfer was studied using UV–visible spectroscopy. From the highest separation rate, the most favorable conditions were observed with 0.2 %?w/v of TX-114, pH?=?9.0, ionic strength of 10 mM NaCl, and incubation at 40 °C for 60 min, yielding an extraction efficiency of 89.2?±?3.9 % and a preconcentration factor of 86. The aggregate size distribution confirmed the formation of very large nCuO–micelle assemblies (11.9 μm) under these conditions. The surface charge of nCuO was also diminished effectively. An extraction efficiency of 91 % was achieved with a mixture of TX-100 and TX-114 containing 30 wt.% of TX-100. Natural organic and particulate matters, represented by humic acid (30 mg/L) and micron-sized silica particles (50 mg/L), respectively, did not significantly reduce the CPE efficiency (<10 %). The recovery of copper(II) ions (20 mg/L) in the presence of humic acid was low (3–10 %). The spiked natural water samples were analyzed either directly or after CPE by inductively coupled plasma mass spectrometry following acid digestion/microwave irradiation. The results indicated the influence of matrix effects and their reduction by CPE. A delay between spiking nCuO and CPE may also influence the recovery of nCuO due to aggregation and dissolution. A detection limit of 0.04 μg Cu/L was achieved for nCuO.     

Relationship between musculoskeletal disorders and anthropometric indices among bus drivers in Zahedan city

Introduction. Most work-related musculoskeletal disorders (MSDs) are associated with improper postures and poorly designed workstations. This study is an attempt to examine the prevalence and severity of MSDs as well as anthropometric dimensions among city bus drivers. Methodology. This cross-sectional study was carried out on 60 male bus drivers. A body discomfort chart was used to evaluate MSDs. Spearman correlation was employed to examine the relationship between body size and the severity of discomfort. Results. Data analysis showed that discomfort reported in the lower back (33.3%), upper back (18.3%) and knee (15%) was severe and there was a direct and significant correlation between the body mass index and the severity of discomfort in the shoulder, arm and hip (p?p?Conclusion. The results of the study can be used to address ergonomic risk factors and reduce their associated disorders. The findings of the study can be used in training programs to educate drivers how to decrease the risk associated with work-related MSDs by adopting appropriate behavior strategies.     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.