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.     

Asparagus ploidy distribution related to climates adaptation in Iran

Environment, Development and Sustainability - Asparagus basic chromosome number is 2n?=?2x?=?20. The high rate of polyploidy in wild asparagus has been found in natural...     

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.     

Concentration and characterization of airborne particles in Tehran’s subway system

Particulate matter is an important air pollutant, especially in closed environments like underground subway stations. In this study, a total of 13 elements were determined from PM₁₀ and PM_2.5 samples collected at two subway stations (Imam Khomeini and Sadeghiye) in Tehran’s subway system. Sampling was conducted in April to August 2011 to measure PM concentrations in platform and adjacent outdoor air of the stations. In the Imam Khomeini station, the average concentrations of PM₁₀ and PM_2.5 were 94.4?±?26.3 and 52.3?±?16.5 μg m^?3 in the platform and 81.8?±?22.2 and 35?±?17.6 μg m^?3 in the outdoor air, respectively. In the Sadeghiye station, mean concentrations of PM₁₀ and PM_2.5 were 87.6?±?23 and 41.3?±?20.4 μg m^?3 in the platform and 73.9?±?17.3 and 30?±?15 μg m^?3, in the outdoor air, respectively. The relative contribution of elemental components in each particle fraction were accounted for 43 % (PM₁₀) and 47.7 % (PM_2.5) in platform of Imam Khomeini station and 15.9 % (PM₁₀) and 18.5 % (PM_2.5) in the outdoor air of this station. Also, at the Sadeghiye station, each fraction accounted for 31.6 % (PM₁₀) and 39.8 % (PM_2.5) in platform and was 11.7 % (PM₁₀) and 14.3 % (PM_2.5) in the outdoor. At the Imam Khomeini station, Fe was the predominant element to represent 32.4 and 36 % of the total mass of PM₁₀ and PM_2.5 in the platform and 11.5 and 13.3 % in the outdoor, respectively. At the Sadeghiye station, this element represented 22.7 and 29.8 % of total mass of PM₁₀ and PM_2.5 in the platform and 8.7 and 10.5 % in the outdoor air, respectively. Other major crustal elements were 5.8 % (PM₁₀) and 5.3 % (PM_2.5) in the Imam Khomeini station platform and 2.3 and 2.4 % in the outdoor air, respectively. The proportion of other minor elements was significantly lower, actually less than 7 % in total samples, and V was the minor concentration in total mass of PM₁₀ and PM_2.5 in both platform stations.     

Design and optimization of a hybrid process based on hollow-fiber membrane/coagulation for wastewater treatment

Environmental Science and Pollution Research - Treatment of textile wastewater using ultrafiltration membranes was carried out in this study. Since membrane fouling is a major operational problem...     

Hematological studies on some Nile fishes,Tilapia nilotica,Lates niloticus,and Labeo niloticus

Hemoglobin content of 3 Nile fishes — Tilapia nilotica Linnaeus, Lates niloticus Linnaeus and Labeo niloticus Forskal— were examined. Clear differences in hemoglobin and hematocrit values were observed, and seasonal variation in the hemoglobin content of Lates niloticus and Labeo niloticus were noticed. There is a correlation between hematocrit value and hemoglobin content of the 3 species examined. The chloride content is higher in the species with lower hemoglobin content.     

Investigation of real-life operating patterns of wood-burning appliances using stack temperature data

ABSTRACT

A study was undertaken to identify patterns of consumer use of outdoor wood boilers or outdoor wood furnaces (technically referred to as outdoor wood-fired hydronic heaters (OWHHs)) and indoor wood stoves (IWSs) to inform the development of performance testing protocols that reflect real-life operating conditions. These devices are manually fed, and their usage protocols are a function of a number of variables, including user habits, household characteristics, and environmental factors. In this study, researchers logged the stack wall temperatures of 4 OWHH and 20 IWS units in the states of New York and Washington over two heating seasons. Stack wall temperature is an indicator of changes in combustion modes. Two algorithms were developed to identify usage modes and cold and warm start refueling events from the stack wall temperature time series. A linear correlation analysis was conducted to evaluate the effect of heat demand on usage patterns. The results and methods presented here will inform the cataloging of typical operational patterns of OWHHs and IWSs as a step in the development of performance testing procedures that represent actual in-home usage patterns.

Implications: Current US regulatory programs for residential wood heating use a certification program to assess emissions and efficiency performance. Testing under this program uses a test that burns 100% of a single, standardized wood fuel charge to assess performance at different steady-state load conditions. This study assessed in-field operational patterns to determine if the current certification approach accurately characterized typical homeowner use patterns. The data from this study can be used to inform revisions to testing methods to increase certification test comparability between lab and field performance.     

A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.