Characterization of ionic composition of TSP and PM10 during the Middle Eastern Dust (MED) storms in Ahvaz, Iran

Because of the recent frequent observations of major dust storms in southwestern cities in Iran such as Ahvaz, and the importance of the ionic composition of particulate matters regarding their health effects, source apportionment, etc., the present work was conducted aiming at characterizing the ionic composition of total suspended particles (TSP) and particles on the order of ～10?μm or less (PM(10)) during dust storms in Ahvaz in April-September 2010. TSP and PM(10) samples were collected and their ionic compositions were determined using an ion chromatography. Mean concentrations of TSP and PM(10) were 1,481.5 and 1,072.9?μg/m(3), respectively. Particle concentrations during the Middle Eastern Dust (MED) days were up to four times higher than those in normal days. Ionic components contributed to only 9.5% and 11.3% of the total mass of TSP and PM(10), respectively. Crustal ions were most abundant during dust days, while secondary ions were dominant during non-dust days. Ca(2+)/Na(+) and Cl(-)/Na(+) ratios can be considered as the indicators for identification of the MED occurrence. It was found that possible chemical forms of NaCl, (NH(4))(2)SO(4), KCl, K(2)SO(4), CaCl(2), Ca(NO(3))(2), and CaSO(4) may exist in TSP. Correlation between the anionic and cationic components suggests slight anion and cation deficiencies in TSP and PM(10) samples, though the deficiencies were negligible.     

Effect of fertilizer application on soil heavy metal concentration

A large amount of chemicals is annually applied at the agricultural soils as fertilizers and pesticides. Such applications may result in the increase of heavy metals particularly Cd, Pb, and As. The objective of this study was to investigate the variability of chemical applications on Cd, Pb, and As concentrations of wheat-cultivated soils. Consequently, a study area was designed and was divided into four subareas (A, B, C, and D). The soil sampling was carried out in 40 points of cultivated durum wheat during the 2006–2007 periods. The samples were taken to the laboratory to measure their heavy metal concentration, soil texture, pH, electrical conductivity, cationic exchange capacity, organic matter, and carbonate contents. The result indicated that Cd, Pb, and As concentrations were increased in the cultivated soils due to fertilizer application. Although the statistical analysis indicates that these heavy metals increased significantly (P value < 0.05), the lead and arsenic concentrations were increased dramatically compared to Cd concentration. This can be related to overapplication of fertilizers as well as the pesticides that are used to replant plant pests, herbs, and rats.     

Improving Estimation of Specific Surface Area by Artificial Neural Network Ensembles Using Fractal and Particle Size Distribution Curve Parameters as Predictors

Specific surface area (SSA) is one of the principal soil properties used in modeling soil processes. In this study, artificial neural network (ANN) ensembles were evaluated to predict SSA. Complete soil particle-size distribution was estimated from sand, silt, and clay fractions using the model by Skaggs et al. and then the particle-size distribution curve parameters (PSDCPs) and fractal parameters were calculated. The PSDCPs were used to predict 20 particle-size classes for a soil sample’s particle size distribution. Fractal parameters were calculated by the model of Bird et al. In addition, total soil-specific surface area (TSS) was calculated using the above 20 size classes. Pedotransfer functions were developed for SSA and TSS using ANN ensembles from 63 pieces of SSA data taken from the literature. Fractal parameters, PSDCPs, and some other soil properties were used to predict SSA and TSS. Introducing fractal parameters and PSDCPs improved the SSA estimations by 12.5 and 11.1 %, respectively. The improvements were even better for TSS estimations (27.7 and 27.0 %, respectively). The use of fractal parameters as estimators described 44 and 92.8 % of the variation in SSA and TSS, respectively, while PSDCPs explained 42 and 6.6 % of the variation in SSA and TSS, respectively. The results suggested that fractal parameters and PSDCPs could be successfully used as predictors in ANN ensembles to predict SSA and TSS.     

Discharge permit market and farm management nexus: an approach for eutrophication control in small basins with low-income farmers

The economic concerns of low-income farmers are barriers to nutrient abatement policies for eutrophication control in surface waters. This study brings up a perspective that focuses on integrating multiple-pollutant discharge permit markets with farm management practices. This aims to identify a more economically motivated waste load allocation (WLA) for non-point sources (NPS). For this purpose, we chose the small basin of Zrebar Lake in western Iran and used the soil and water assessment tool (SWAT) for modeling. The export coefficients (ECs), effectiveness of best management practices (BMPs), and crop yields were calculated by using this software. These variables show that low-income farmers can hardly afford to invest in BMPs in a typical WLA. Conversely, a discharge permit market presents a more cost-effective solution. This method saves 64% in total abatement costs and motivates farmers by offering economic benefits. A market analysis revealed that nitrogen permits mostly cover the trades with the optimal price ranging from $6 to $30 per kilogram. However, phosphorous permits are limited for trading, and their price exceeds $60 per kilogram. This approach also emphasizes the establishment of a regional institution for market monitoring, dynamic pricing, fair fund reallocation, giving information to participants, and ensuring their income. By these sets of strategies, a WLA on the brink of failure can turn into a cost-effective and sustainable policy for eutrophication control in small basins.     

SPME-based air sampling method for inhalation exposure assessment studies: case study on perchlorethylene exposure in dry cleaning

Exposure to perchlorethylene, especially for dry cleaning workers and for people living near dry cleaning shops, could lead to several diseases and disorders. This study examines the value of solid-phase microextraction (SPME) for sampling perchlorethylene in the atmosphere of dry cleaning shops. Carboxen/polydimethylsiloxane (CAR/PDMS) in 0.5-cm retracted mode was selected. There were no significant differences between sampling rates at different temperatures (range of 20 to 30 °C) and air velocities (2 to 50 cm/s). On the opposite, relative humidity (RH) had a significant effect on sampling rates. Method reproducibility was realized in the laboratory and field conditions and was 6.2 % and 7 to 11 %, respectively. Repeatability was also determined as 8.9 %. Comparison of the results according to the American Industrial Hygiene Association exposure assessment strategy showed the SPME sampler yields more conservative results in comparison with traditional standard method.     

Mass transfer and bioremediation of aromatics from NAPL in a baffled roller bioreactor

A non-aqueous phase liquid (NAPL) containing dissolved naphthalene or phenol was used to simulate water insoluble contaminants which are produced during the processing of oil sands. Mass transfer and biodegradation of organic contaminants in the aqueous phase were studied in a baffled roller bioreactor. Mass transfer of both naphthalene and phenol from NAPL into the aqueous phase was completed in less than 60 min, by which time naphthalene reached its saturation concentration in the aqueous phase and phenol was completely transferred into the aqueous phase. Pseudomonas putida (ATCC 17484) was subsequently used in biodegradation experiments in the baffled bioreactor containing the model NAPL contaminant. The optimum loading of NAPL for biodegradation of naphthalene at 500 mg/L was found to be 40%. High biodegradation rates (136.4 mg/L h for naphthalene and 13.2 mg/L h for phenol based on the working volume of the bioreactor) were achieved. In the case of simultaneous biodegradation of naphthalene and phenol, the highest total biodegradation rate of 102.6 mg/L h was achieved.     

Leaching kinetics of valuable metals from waste Li-ion batteries using neural network approach

Journal of Material Cycles and Waste Management - The kinetic study of valuable metals recovery from waste lithium-ion batteries (LIBs) using the artificial neural network (ANN) was investigated. A...     

Hydraulics Near Unscreened Diversion Pipes in Open Channels: Large Flume Experiments

Most of the water diversions on the Sacramento and San Joaquin Rivers (California, United States) and their tributaries are currently unscreened. These unscreened diversions are commonly used for irrigation and are potentially harmful to migrating and resident fishes. A large flume (test section: 18.29 m long, 3.05 m wide and 3.20 m high) was used to investigate the hydraulic fields near an unscreened water diversion under ecologically and hydraulically relevant diversion rates and channel flow characteristics. We investigated all combinations of three diversion rates (0.28, 0.42, and 0.57 m³/s) and three sweeping velocities (0.15, 0.38, and 0.61 m/s), with one additional test at 0.71 m³/s and 0.15 m/s. We measured the three‐dimensional velocity field at seven cross sections near a diversion pipe and constructed regression equations of the observed maximum velocities near the pipe. Because the velocity components in three directions (longitudinal, transverse, and vertical) were significantly greater near the diversion pipe inlet compared with those farther from it, they cannot be neglected in the modeling and design of fish guidance and protection devices for diversion pipes. Our results should be of great value in quantifying the hydraulic fields that are formed around fish guidance devices to design more effective protection for fishes from entrainment into unscreened water‐diversion pipes.     

Sustainable cropping pattern in North Iran: application of fuzzy goal programming

Due to the important role that the application of mathematical programming models have in determining optimal cropping patterns, this research presents a sustainable cropping pattern that considers selected economic, environmental, and social goals together. Using a random sampling method, a sample size of 168 farmers was selected in the Sari County, Iran. Our results showed that economic, self-sufficiency, environmental, and social goals have a distinctly different impact on cropping pattern performance. Compared to the current cropping pattern, the gross margins for economic and social goals increased by nearly 11 and 2 %, respectively, and the gross margins for self-sufficiency and environmental goals decreased by nearly 2 and 36 %. Interestingly, it has been found that the performance of the current cropping pattern has an average positive impact of 6 % if economic, self-sufficiency, environmental, and social (employment) goals are realized simultaneously.     

Fingerprinting and Groundwater Model Application to Evaluate Hydraulic Barrier Efficiency (Italy)

Remediation actions at contaminated sites are based on multiple numerical model scenarios considering different parameter distributions, source positions and contaminant transport paths. In some cases the excess of scenarios is due to uncertainties in the conceptual model as a result of the spread of contamination through heterogeneities in the physical system. Reduction of project hypotheses and conceptual model uncertainty is therefore needed. This result can be achieved by coupling hydrogeological investigations with environmental forensic techniques, better localization of the source and understanding of contamination history. In this respect, in the present study, compositional fingerprinting and groundwater flow modeling were applied to a former oil storage facility where, even though a hydraulic barrier had been built to stop the hydrocarbon plume, the presence of some hydrocarbons was still found in downgradient monitoring wells. The final aim was to evaluate the efficacy of the hydraulic barrier and identify of the source of pollution. Fingerprinting results indicated pollution with a gasoline-diesel mixture much altered by water washing and/or biodegradation. Comparison of seven groundwater samples collected in wells and monitoring wells was performed by analyzing the volatile fraction (BTEX) and the total ion chromatogram (TIC), focusing attention on: n-alkanes (m/z 85), alkylcyclohexanes (m/z 83), isoprenoids (m/z 113), C4-alkylbenzenes (m/z 134), C3-C6 alkylbenzenes and polycyclic aromatic hydrocarbons (PAHs). The most probable scenario was then identified by combining the results of fingerprinting with different contaminant paths obtained using the numerical model.